AGEO PLAGAST_Clinical Final analysis_revisions 05042025
library(swimplot) library(coxphf) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(ComplexHeatmap) library(tidyverse) library(readxl) library(survival) library(janitor) library(openxlsx) library(writexl) library(rms) library(pROC) library(DT)
#ctDNA Detection rate by Stage and Window
#Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#C2D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.C2D1 %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C2D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C2D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C2D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#post-NAC Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postNAC %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postNAC == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postNAC, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postNAC == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#On-treatment
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.onACT <- factor(circ_data$ctDNA.onACT, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.onACT %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.onACT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.onACT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.onACT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.surveillance %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Post-ACT Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postACT <- factor(circ_data$ctDNA.postACT, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postACT %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postACT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postACT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postACT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#Post-relapse Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postRelapse <- factor(circ_data$ctDNA.postRelapse, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postRelapse %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postRelapse == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postRelapse, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postRelapse == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)#Neoadjuvant Treatment duration
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE, ]
circ_data$NAC.Start <- as.Date(circ_data$NAC.Start, format="%m/%d/%Y")
circ_data$NAC.End <- as.Date(circ_data$NAC.End, format="%m/%d/%Y")
circ_data$NAC.Duration <- as.numeric(difftime(circ_data$NAC.End, circ_data$NAC.Start, units="days"))
median_duration <- median(circ_data$NAC.Duration, na.rm=TRUE)
range_duration <- range(circ_data$NAC.Duration, na.rm=TRUE)
Q1 <- quantile(circ_data$NAC.Duration, 0.25, na.rm=TRUE)
Q3 <- quantile(circ_data$NAC.Duration, 0.75, na.rm=TRUE)
IQR_value <- IQR(circ_data$NAC.Duration, na.rm=TRUE)
cat("Median NAC Duration:", median_duration, "days\n")Median NAC Duration: 43 dayscat("Range of NAC Duration:", range_duration[1], "-", range_duration[2], "days\n")Range of NAC Duration: 28 - 242 dayscat("Q1 (25th percentile):", Q1, "days\n")Q1 (25th percentile): 42 dayscat("Q3 (75th percentile):", Q3, "days\n")Q3 (75th percentile): 56 dayscat("Interquartile Range (IQR):", IQR_value, "days\n")Interquartile Range (IQR): 14 days#Time of ctDNA draws from start and end of neodjuvant treatment
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE, ]
circ_data$NAC.Start <- as.Date(circ_data$NAC.Start, format="%m/%d/%Y")
circ_data$NAC.End <- as.Date(circ_data$NAC.End, format="%m/%d/%Y")
circ_data$ctDNA.C2D1.Date <- as.Date(circ_data$ctDNA.C2D1.Date, format="%m/%d/%Y")
circ_data$ctDNA.postNAC.Date <- as.Date(circ_data$ctDNA.postNAC.Date, format="%m/%d/%Y")
circ_data$Time_to_ctDNA_C2D1 <- as.numeric(difftime(circ_data$ctDNA.C2D1.Date, circ_data$NAC.Start, units="days"))
circ_data$Time_to_ctDNA_postNAC <- as.numeric(difftime(circ_data$ctDNA.postNAC.Date, circ_data$NAC.End, units="days"))
median_ctDNA_C2D1 <- median(circ_data$Time_to_ctDNA_C2D1, na.rm=TRUE)
range_ctDNA_C2D1 <- range(circ_data$Time_to_ctDNA_C2D1, na.rm=TRUE)
Q1_ctDNA_C2D1 <- quantile(circ_data$Time_to_ctDNA_C2D1, 0.25, na.rm=TRUE)
Q3_ctDNA_C2D1 <- quantile(circ_data$Time_to_ctDNA_C2D1, 0.75, na.rm=TRUE)
IQR_ctDNA_C2D1 <- IQR(circ_data$Time_to_ctDNA_C2D1, na.rm=TRUE)
median_ctDNA_postNAC <- median(circ_data$Time_to_ctDNA_postNAC, na.rm=TRUE)
range_ctDNA_postNAC <- range(circ_data$Time_to_ctDNA_postNAC, na.rm=TRUE)
Q1_ctDNA_postNAC <- quantile(circ_data$Time_to_ctDNA_postNAC, 0.25, na.rm=TRUE)
Q3_ctDNA_postNAC <- quantile(circ_data$Time_to_ctDNA_postNAC, 0.75, na.rm=TRUE)
IQR_ctDNA_postNAC <- IQR(circ_data$Time_to_ctDNA_postNAC, na.rm=TRUE)
cat("Median time from NAC start to ctDNA draw during NAC:", median_ctDNA_C2D1, "days\n")Median time from NAC start to ctDNA draw during NAC: 14 dayscat("Range:", range_ctDNA_C2D1[1], "-", range_ctDNA_C2D1[2], "days\n")Range: 10 - 111 dayscat("Q1 (25th percentile):", Q1_ctDNA_C2D1, "days\n")Q1 (25th percentile): 14 dayscat("Q3 (75th percentile):", Q3_ctDNA_C2D1, "days\n")Q3 (75th percentile): 28 dayscat("Interquartile Range (IQR):", IQR_ctDNA_C2D1, "days\n\n")Interquartile Range (IQR): 14 dayscat("Median time from NAC end to ctDNA draw post-NAC:", median_ctDNA_postNAC, "days\n")Median time from NAC end to ctDNA draw post-NAC: 0 dayscat("Range:", range_ctDNA_postNAC[1], "-", range_ctDNA_postNAC[2], "days\n")Range: -4 - 64 dayscat("Q1 (25th percentile):", Q1_ctDNA_postNAC, "days\n")Q1 (25th percentile): 0 dayscat("Q3 (75th percentile):", Q3_ctDNA_postNAC, "days\n")Q3 (75th percentile): 12 dayscat("Interquartile Range (IQR):", IQR_ctDNA_postNAC, "days\n")Interquartile Range (IQR): 12 days#Demographics Table
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data_subset <- circ_data %>%
select(
Age,
Gender,
PrimSite,
NAC,
NAC.Regimen,
TRG.Mandard,
TNM,
Stage,
Grade,
Lauren.Class,
Margins,
ACT,
ACT.Regimen,
DFS.Event,
OS.Event,
FU.months) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
PrimSite = factor(PrimSite, levels = c("Stomach", "G/J", "Oesophagus")),
NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Therapy", "Upfront Surgery")),
NAC.Regimen = factor(NAC.Regimen),
TRG.Mandard = factor(TRG.Mandard, levels = c("TRG1","TRG2", "TRG3", "TRG4", "TRG5")),
TNM = factor(TNM, levels = c("T0-TisN0M0","T1-T2N0", "T2-T3N0-N1", "T2N1-N2", "T3N2-N3", "T4N0-N1", "T4N2-N3")),
Stage = factor(Stage, levels = c("0","I","II", "III")),
Grade = factor(Grade, levels = c("G1", "G2", "G3")),
Lauren.Class = factor(Lauren.Class),
Margins = factor(Margins, levels = c("R0", "R1")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Treatment", "Observation")),
ACT.Regimen = factor(ACT.Regimen),
DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
FU.months = as.numeric(FU.months))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 621 |
|---|---|
| Age | 66 (34 - 86) |
| Gender | |
| Â Â Â Â Male | 39 (63%) |
| Â Â Â Â Female | 23 (37%) |
| PrimSite | |
| Â Â Â Â Stomach | 38 (61%) |
| Â Â Â Â G/J | 24 (39%) |
| Â Â Â Â Oesophagus | 0 (0%) |
| NAC | |
| Â Â Â Â Neoadjuvant Therapy | 55 (89%) |
| Â Â Â Â Upfront Surgery | 7 (11%) |
| NAC.Regimen | |
| Â Â Â Â | 7 (11%) |
| Â Â Â Â Chemoimmunotherapy | 7 (11%) |
| Â Â Â Â Chemotherapy | 47 (76%) |
| Â Â Â Â Radiotherapy | 1 (1.6%) |
| TRG.Mandard | |
| Â Â Â Â TRG1 | 3 (5.5%) |
| Â Â Â Â TRG2 | 8 (15%) |
| Â Â Â Â TRG3 | 21 (38%) |
| Â Â Â Â TRG4 | 16 (29%) |
| Â Â Â Â TRG5 | 7 (13%) |
| Â Â Â Â Unknown | 7 |
| TNM | |
| Â Â Â Â T0-TisN0M0 | 3 (4.8%) |
| Â Â Â Â T1-T2N0 | 16 (26%) |
| Â Â Â Â T2-T3N0-N1 | 17 (27%) |
| Â Â Â Â T2N1-N2 | 2 (3.2%) |
| Â Â Â Â T3N2-N3 | 8 (13%) |
| Â Â Â Â T4N0-N1 | 9 (15%) |
| Â Â Â Â T4N2-N3 | 7 (11%) |
| Stage | |
| Â Â Â Â 0 | 3 (4.8%) |
| Â Â Â Â I | 16 (26%) |
| Â Â Â Â II | 26 (42%) |
| Â Â Â Â III | 17 (27%) |
| Grade | |
| Â Â Â Â G1 | 11 (28%) |
| Â Â Â Â G2 | 21 (53%) |
| Â Â Â Â G3 | 8 (20%) |
| Â Â Â Â Unknown | 22 |
| Lauren.Class | |
| Â Â Â Â | 5 (8.1%) |
| Â Â Â Â Diffuse | 18 (29%) |
| Â Â Â Â Intestinal | 34 (55%) |
| Â Â Â Â Mixed | 5 (8.1%) |
| Margins | |
| Â Â Â Â R0 | 61 (98%) |
| Â Â Â Â R1 | 1 (1.6%) |
| ACT | |
| Â Â Â Â Adjuvant Treatment | 53 (85%) |
| Â Â Â Â Observation | 9 (15%) |
| ACT.Regimen | |
| Â Â Â Â | 9 (15%) |
| Â Â Â Â Chemoimmunotherapy | 4 (6.5%) |
| Â Â Â Â Chemotherapy | 48 (77%) |
| Â Â Â Â Immunotherapy | 1 (1.6%) |
| DFS.Event | |
| Â Â Â Â Recurrence | 29 (47%) |
| Â Â Â Â No Recurrence | 33 (53%) |
| OS.Event | |
| Â Â Â Â Deceased | 19 (31%) |
| Â Â Â Â Alive | 43 (69%) |
| FU.months | 29 (2 - 93) |
| 1 Median (Min - Max); n (%) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE
)
fit1Characteristic | N = 621 |
|---|---|
Age | 66 (34 - 86) |
Gender | |
Male | 39 (63%) |
Female | 23 (37%) |
PrimSite | |
Stomach | 38 (61%) |
G/J | 24 (39%) |
Oesophagus | 0 (0%) |
NAC | |
Neoadjuvant Therapy | 55 (89%) |
Upfront Surgery | 7 (11%) |
NAC.Regimen | |
7 (11%) | |
Chemoimmunotherapy | 7 (11%) |
Chemotherapy | 47 (76%) |
Radiotherapy | 1 (1.6%) |
TRG.Mandard | |
TRG1 | 3 (5.5%) |
TRG2 | 8 (15%) |
TRG3 | 21 (38%) |
TRG4 | 16 (29%) |
TRG5 | 7 (13%) |
Unknown | 7 |
TNM | |
T0-TisN0M0 | 3 (4.8%) |
T1-T2N0 | 16 (26%) |
T2-T3N0-N1 | 17 (27%) |
T2N1-N2 | 2 (3.2%) |
T3N2-N3 | 8 (13%) |
T4N0-N1 | 9 (15%) |
T4N2-N3 | 7 (11%) |
Stage | |
0 | 3 (4.8%) |
I | 16 (26%) |
II | 26 (42%) |
III | 17 (27%) |
Grade | |
G1 | 11 (28%) |
G2 | 21 (53%) |
G3 | 8 (20%) |
Unknown | 22 |
Lauren.Class | |
5 (8.1%) | |
Diffuse | 18 (29%) |
Intestinal | 34 (55%) |
Mixed | 5 (8.1%) |
Margins | |
R0 | 61 (98%) |
R1 | 1 (1.6%) |
ACT | |
Adjuvant Treatment | 53 (85%) |
Observation | 9 (15%) |
ACT.Regimen | |
9 (15%) | |
Chemoimmunotherapy | 4 (6.5%) |
Chemotherapy | 48 (77%) |
Immunotherapy | 1 (1.6%) |
DFS.Event | |
Recurrence | 29 (47%) |
No Recurrence | 33 (53%) |
OS.Event | |
Deceased | 19 (31%) |
Alive | 43 (69%) |
FU.months | 29 (2 - 93) |
1Median (Min - Max); n (%) | |
save_as_docx(fit1, path= "~/Downloads/table1.docx")#Demographics Table for those that included vs excluded from the study
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data_subset1 <- circ_data %>%
select(
Age,
Gender,
PrimSite,
NAC,
NAC.Regimen,
TRG.Mandard,
TNM,
Stage,
Grade,
Lauren.Class,
Margins,
ACT,
ACT.Regimen,
DFS.Event,
OS.Event,
FU.months) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
PrimSite = factor(PrimSite, levels = c("Stomach", "G/J", "Oesophagus")),
NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Therapy", "Upfront Surgery")),
NAC.Regimen = factor(NAC.Regimen),
TRG.Mandard = factor(TRG.Mandard, levels = c("TRG1","TRG2", "TRG3", "TRG4", "TRG5")),
TNM = factor(TNM, levels = c("T0-TisN0M0","T1-T2N0", "T2-T3N0-N1", "T2N1-N2", "T3N2-N3", "T4N0-N1", "T4N2-N3")),
Stage = factor(Stage, levels = c("0","I","II", "III")),
Grade = factor(Grade, levels = c("G1", "G2", "G3")),
Lauren.Class = factor(Lauren.Class),
Margins = factor(Margins, levels = c("R0", "R1")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Treatment", "Observation")),
ACT.Regimen = factor(ACT.Regimen),
DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
FU.months = as.numeric(FU.months))
circ_data1 <- read.csv("PLAGAST_Clinical Data.csv")
circ_data_subset2 <- circ_data %>%
select(
Age,
Gender,
PrimSite,
NAC,
NAC.Regimen,
TRG.Mandard,
TNM,
Stage,
Grade,
Lauren.Class,
Margins,
ACT,
ACT.Regimen,
DFS.Event,
OS.Event,
FU.months,
Included) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
PrimSite = factor(PrimSite, levels = c("Stomach", "G/J", "Oesophagus")),
NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Therapy", "Upfront Surgery")),
NAC.Regimen = factor(NAC.Regimen),
TRG.Mandard = factor(TRG.Mandard, levels = c("TRG1","TRG2", "TRG3", "TRG4", "TRG5")),
TNM = factor(TNM, levels = c("T0-TisN0M0","T1-T2N0", "T2-T3N0-N1", "T2N1-N2", "T3N2-N3", "T4N0-N1", "T4N2-N3")),
Stage = factor(Stage, levels = c("0","I","II", "III")),
Grade = factor(Grade, levels = c("G1", "G2", "G3")),
Lauren.Class = factor(Lauren.Class),
Margins = factor(Margins, levels = c("R0", "R1")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Treatment", "Observation")),
ACT.Regimen = factor(ACT.Regimen),
DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
FU.months = as.numeric(FU.months),
Included = factor(Included, levels = c("TRUE", "FALSE"), labels = c("Included", "Excluded")))
Overall <- circ_data_subset1 %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
Overall| Characteristic | N = 821 |
|---|---|
| Age | 67 (34 - 86) |
| Gender | |
| Â Â Â Â Male | 51 (62%) |
| Â Â Â Â Female | 31 (38%) |
| PrimSite | |
| Â Â Â Â Stomach | 54 (66%) |
| Â Â Â Â G/J | 28 (34%) |
| Â Â Â Â Oesophagus | 0 (0%) |
| NAC | |
| Â Â Â Â Neoadjuvant Therapy | 74 (90%) |
| Â Â Â Â Upfront Surgery | 8 (9.8%) |
| NAC.Regimen | |
| Â Â Â Â | 8 (9.8%) |
| Â Â Â Â Chemoimmunotherapy | 8 (9.8%) |
| Â Â Â Â Chemotherapy | 65 (79%) |
| Â Â Â Â Radiotherapy | 1 (1.2%) |
| TRG.Mandard | |
| Â Â Â Â TRG1 | 4 (5.6%) |
| Â Â Â Â TRG2 | 14 (19%) |
| Â Â Â Â TRG3 | 26 (36%) |
| Â Â Â Â TRG4 | 19 (26%) |
| Â Â Â Â TRG5 | 9 (13%) |
| Â Â Â Â Unknown | 10 |
| TNM | |
| Â Â Â Â T0-TisN0M0 | 3 (3.7%) |
| Â Â Â Â T1-T2N0 | 25 (30%) |
| Â Â Â Â T2-T3N0-N1 | 21 (26%) |
| Â Â Â Â T2N1-N2 | 4 (4.9%) |
| Â Â Â Â T3N2-N3 | 9 (11%) |
| Â Â Â Â T4N0-N1 | 12 (15%) |
| Â Â Â Â T4N2-N3 | 8 (9.8%) |
| Stage | |
| Â Â Â Â 0 | 3 (3.7%) |
| Â Â Â Â I | 25 (30%) |
| Â Â Â Â II | 33 (40%) |
| Â Â Â Â III | 21 (26%) |
| Grade | |
| Â Â Â Â G1 | 14 (26%) |
| Â Â Â Â G2 | 27 (50%) |
| Â Â Â Â G3 | 13 (24%) |
| Â Â Â Â Unknown | 28 |
| Lauren.Class | |
| Â Â Â Â | 8 (9.8%) |
| Â Â Â Â Diffuse | 21 (26%) |
| Â Â Â Â Intestinal | 45 (55%) |
| Â Â Â Â Mixed | 8 (9.8%) |
| Margins | |
| Â Â Â Â R0 | 78 (95%) |
| Â Â Â Â R1 | 4 (4.9%) |
| ACT | |
| Â Â Â Â Adjuvant Treatment | 68 (83%) |
| Â Â Â Â Observation | 14 (17%) |
| ACT.Regimen | |
| Â Â Â Â | 19 (23%) |
| Â Â Â Â Chemoimmunotherapy | 4 (4.9%) |
| Â Â Â Â Chemotherapy | 58 (71%) |
| Â Â Â Â Immunotherapy | 1 (1.2%) |
| DFS.Event | |
| Â Â Â Â Recurrence | 37 (45%) |
| Â Â Â Â No Recurrence | 45 (55%) |
| OS.Event | |
| Â Â Â Â Deceased | 25 (30%) |
| Â Â Â Â Alive | 57 (70%) |
| FU.months | 32 (2 - 93) |
| 1 Median (Min - Max); n (%) | |
ByctDNA_MRD <- circ_data_subset2 %>%
tbl_summary(
by = Included, # Subgroup by Included
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
add_p(test = all_categorical() ~ "fisher.test", pvalue_fun = ~style_pvalue(p.adjust(.x, method = "bonferroni"))) %>%
bold_labels()
ByctDNA_MRD| Characteristic | Included N = 621 |
Excluded N = 201 |
p-value2 |
|---|---|---|---|
| Age | 66 (34 - 86) | 70 (44 - 82) | 0.8 |
| Gender | >0.9 | ||
| Â Â Â Â Male | 39 (63%) | 12 (60%) | |
| Â Â Â Â Female | 23 (37%) | 8 (40%) | |
| PrimSite | >0.9 | ||
| Â Â Â Â Stomach | 38 (61%) | 16 (80%) | |
| Â Â Â Â G/J | 24 (39%) | 4 (20%) | |
| Â Â Â Â Oesophagus | 0 (0%) | 0 (0%) | |
| NAC | >0.9 | ||
| Â Â Â Â Neoadjuvant Therapy | 55 (89%) | 19 (95%) | |
| Â Â Â Â Upfront Surgery | 7 (11%) | 1 (5.0%) | |
| NAC.Regimen | >0.9 | ||
| Â Â Â Â | 7 (11%) | 1 (5.0%) | |
| Â Â Â Â Chemoimmunotherapy | 7 (11%) | 1 (5.0%) | |
| Â Â Â Â Chemotherapy | 47 (76%) | 18 (90%) | |
| Â Â Â Â Radiotherapy | 1 (1.6%) | 0 (0%) | |
| TRG.Mandard | >0.9 | ||
| Â Â Â Â TRG1 | 3 (5.5%) | 1 (5.9%) | |
| Â Â Â Â TRG2 | 8 (15%) | 6 (35%) | |
| Â Â Â Â TRG3 | 21 (38%) | 5 (29%) | |
| Â Â Â Â TRG4 | 16 (29%) | 3 (18%) | |
| Â Â Â Â TRG5 | 7 (13%) | 2 (12%) | |
| Â Â Â Â Unknown | 7 | 3 | |
| TNM | >0.9 | ||
| Â Â Â Â T0-TisN0M0 | 3 (4.8%) | 0 (0%) | |
| Â Â Â Â T1-T2N0 | 16 (26%) | 9 (45%) | |
| Â Â Â Â T2-T3N0-N1 | 17 (27%) | 4 (20%) | |
| Â Â Â Â T2N1-N2 | 2 (3.2%) | 2 (10%) | |
| Â Â Â Â T3N2-N3 | 8 (13%) | 1 (5.0%) | |
| Â Â Â Â T4N0-N1 | 9 (15%) | 3 (15%) | |
| Â Â Â Â T4N2-N3 | 7 (11%) | 1 (5.0%) | |
| Stage | >0.9 | ||
| Â Â Â Â 0 | 3 (4.8%) | 0 (0%) | |
| Â Â Â Â I | 16 (26%) | 9 (45%) | |
| Â Â Â Â II | 26 (42%) | 7 (35%) | |
| Â Â Â Â III | 17 (27%) | 4 (20%) | |
| Grade | >0.9 | ||
| Â Â Â Â G1 | 11 (28%) | 3 (21%) | |
| Â Â Â Â G2 | 21 (53%) | 6 (43%) | |
| Â Â Â Â G3 | 8 (20%) | 5 (36%) | |
| Â Â Â Â Unknown | 22 | 6 | |
| Lauren.Class | >0.9 | ||
| Â Â Â Â | 5 (8.1%) | 3 (15%) | |
| Â Â Â Â Diffuse | 18 (29%) | 3 (15%) | |
| Â Â Â Â Intestinal | 34 (55%) | 11 (55%) | |
| Â Â Â Â Mixed | 5 (8.1%) | 3 (15%) | |
| Margins | 0.7 | ||
| Â Â Â Â R0 | 61 (98%) | 17 (85%) | |
| Â Â Â Â R1 | 1 (1.6%) | 3 (15%) | |
| ACT | >0.9 | ||
| Â Â Â Â Adjuvant Treatment | 53 (85%) | 15 (75%) | |
| Â Â Â Â Observation | 9 (15%) | 5 (25%) | |
| ACT.Regimen | 0.2 | ||
| Â Â Â Â | 9 (15%) | 10 (50%) | |
| Â Â Â Â Chemoimmunotherapy | 4 (6.5%) | 0 (0%) | |
| Â Â Â Â Chemotherapy | 48 (77%) | 10 (50%) | |
| Â Â Â Â Immunotherapy | 1 (1.6%) | 0 (0%) | |
| DFS.Event | >0.9 | ||
| Â Â Â Â Recurrence | 29 (47%) | 8 (40%) | |
| Â Â Â Â No Recurrence | 33 (53%) | 12 (60%) | |
| OS.Event | >0.9 | ||
| Â Â Â Â Deceased | 19 (31%) | 6 (30%) | |
| Â Â Â Â Alive | 43 (69%) | 14 (70%) | |
| FU.months | 29 (2 - 93) | 34 (7 - 68) | >0.9 |
| 1 Median (Min - Max); n (%) | |||
| 2 Wilcoxon rank sum test; Fisher’s exact test | |||
merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table| Characteristic |
Table 1
|
Table 2
|
||
|---|---|---|---|---|
| N = 821 | Included N = 621 |
Excluded N = 201 |
p-value2 | |
| Age | 67 (34 - 86) | 66 (34 - 86) | 70 (44 - 82) | 0.8 |
| Gender | >0.9 | |||
| Â Â Â Â Male | 51 (62%) | 39 (63%) | 12 (60%) | |
| Â Â Â Â Female | 31 (38%) | 23 (37%) | 8 (40%) | |
| PrimSite | >0.9 | |||
| Â Â Â Â Stomach | 54 (66%) | 38 (61%) | 16 (80%) | |
| Â Â Â Â G/J | 28 (34%) | 24 (39%) | 4 (20%) | |
| Â Â Â Â Oesophagus | 0 (0%) | 0 (0%) | 0 (0%) | |
| NAC | >0.9 | |||
| Â Â Â Â Neoadjuvant Therapy | 74 (90%) | 55 (89%) | 19 (95%) | |
| Â Â Â Â Upfront Surgery | 8 (9.8%) | 7 (11%) | 1 (5.0%) | |
| NAC.Regimen | >0.9 | |||
| Â Â Â Â | 8 (9.8%) | 7 (11%) | 1 (5.0%) | |
| Â Â Â Â Chemoimmunotherapy | 8 (9.8%) | 7 (11%) | 1 (5.0%) | |
| Â Â Â Â Chemotherapy | 65 (79%) | 47 (76%) | 18 (90%) | |
| Â Â Â Â Radiotherapy | 1 (1.2%) | 1 (1.6%) | 0 (0%) | |
| TRG.Mandard | >0.9 | |||
| Â Â Â Â TRG1 | 4 (5.6%) | 3 (5.5%) | 1 (5.9%) | |
| Â Â Â Â TRG2 | 14 (19%) | 8 (15%) | 6 (35%) | |
| Â Â Â Â TRG3 | 26 (36%) | 21 (38%) | 5 (29%) | |
| Â Â Â Â TRG4 | 19 (26%) | 16 (29%) | 3 (18%) | |
| Â Â Â Â TRG5 | 9 (13%) | 7 (13%) | 2 (12%) | |
| Â Â Â Â Unknown | 10 | 7 | 3 | |
| TNM | >0.9 | |||
| Â Â Â Â T0-TisN0M0 | 3 (3.7%) | 3 (4.8%) | 0 (0%) | |
| Â Â Â Â T1-T2N0 | 25 (30%) | 16 (26%) | 9 (45%) | |
| Â Â Â Â T2-T3N0-N1 | 21 (26%) | 17 (27%) | 4 (20%) | |
| Â Â Â Â T2N1-N2 | 4 (4.9%) | 2 (3.2%) | 2 (10%) | |
| Â Â Â Â T3N2-N3 | 9 (11%) | 8 (13%) | 1 (5.0%) | |
| Â Â Â Â T4N0-N1 | 12 (15%) | 9 (15%) | 3 (15%) | |
| Â Â Â Â T4N2-N3 | 8 (9.8%) | 7 (11%) | 1 (5.0%) | |
| Stage | >0.9 | |||
| Â Â Â Â 0 | 3 (3.7%) | 3 (4.8%) | 0 (0%) | |
| Â Â Â Â I | 25 (30%) | 16 (26%) | 9 (45%) | |
| Â Â Â Â II | 33 (40%) | 26 (42%) | 7 (35%) | |
| Â Â Â Â III | 21 (26%) | 17 (27%) | 4 (20%) | |
| Grade | >0.9 | |||
| Â Â Â Â G1 | 14 (26%) | 11 (28%) | 3 (21%) | |
| Â Â Â Â G2 | 27 (50%) | 21 (53%) | 6 (43%) | |
| Â Â Â Â G3 | 13 (24%) | 8 (20%) | 5 (36%) | |
| Â Â Â Â Unknown | 28 | 22 | 6 | |
| Lauren.Class | >0.9 | |||
| Â Â Â Â | 8 (9.8%) | 5 (8.1%) | 3 (15%) | |
| Â Â Â Â Diffuse | 21 (26%) | 18 (29%) | 3 (15%) | |
| Â Â Â Â Intestinal | 45 (55%) | 34 (55%) | 11 (55%) | |
| Â Â Â Â Mixed | 8 (9.8%) | 5 (8.1%) | 3 (15%) | |
| Margins | 0.7 | |||
| Â Â Â Â R0 | 78 (95%) | 61 (98%) | 17 (85%) | |
| Â Â Â Â R1 | 4 (4.9%) | 1 (1.6%) | 3 (15%) | |
| ACT | >0.9 | |||
| Â Â Â Â Adjuvant Treatment | 68 (83%) | 53 (85%) | 15 (75%) | |
| Â Â Â Â Observation | 14 (17%) | 9 (15%) | 5 (25%) | |
| ACT.Regimen | 0.2 | |||
| Â Â Â Â | 19 (23%) | 9 (15%) | 10 (50%) | |
| Â Â Â Â Chemoimmunotherapy | 4 (4.9%) | 4 (6.5%) | 0 (0%) | |
| Â Â Â Â Chemotherapy | 58 (71%) | 48 (77%) | 10 (50%) | |
| Â Â Â Â Immunotherapy | 1 (1.2%) | 1 (1.6%) | 0 (0%) | |
| DFS.Event | >0.9 | |||
| Â Â Â Â Recurrence | 37 (45%) | 29 (47%) | 8 (40%) | |
| Â Â Â Â No Recurrence | 45 (55%) | 33 (53%) | 12 (60%) | |
| OS.Event | >0.9 | |||
| Â Â Â Â Deceased | 25 (30%) | 19 (31%) | 6 (30%) | |
| Â Â Â Â Alive | 57 (70%) | 43 (69%) | 14 (70%) | |
| FU.months | 32 (2 - 93) | 29 (2 - 93) | 34 (7 - 68) | >0.9 |
| 1 Median (Min - Max); n (%) | ||||
| 2 Wilcoxon rank sum test; Fisher’s exact test | ||||
fit1 <- as_flex_table(
merged_table,
include = everything(),
return_calls = FALSE)
fit1
| Table 1 | Table 2 | ||
|---|---|---|---|---|
Characteristic | N = 821 | Included | Excluded | p-value2 |
Age | 67 (34 - 86) | 66 (34 - 86) | 70 (44 - 82) | 0.8 |
Gender | >0.9 | |||
Male | 51 (62%) | 39 (63%) | 12 (60%) | |
Female | 31 (38%) | 23 (37%) | 8 (40%) | |
PrimSite | >0.9 | |||
Stomach | 54 (66%) | 38 (61%) | 16 (80%) | |
G/J | 28 (34%) | 24 (39%) | 4 (20%) | |
Oesophagus | 0 (0%) | 0 (0%) | 0 (0%) | |
NAC | >0.9 | |||
Neoadjuvant Therapy | 74 (90%) | 55 (89%) | 19 (95%) | |
Upfront Surgery | 8 (9.8%) | 7 (11%) | 1 (5.0%) | |
NAC.Regimen | >0.9 | |||
8 (9.8%) | 7 (11%) | 1 (5.0%) | ||
Chemoimmunotherapy | 8 (9.8%) | 7 (11%) | 1 (5.0%) | |
Chemotherapy | 65 (79%) | 47 (76%) | 18 (90%) | |
Radiotherapy | 1 (1.2%) | 1 (1.6%) | 0 (0%) | |
TRG.Mandard | >0.9 | |||
TRG1 | 4 (5.6%) | 3 (5.5%) | 1 (5.9%) | |
TRG2 | 14 (19%) | 8 (15%) | 6 (35%) | |
TRG3 | 26 (36%) | 21 (38%) | 5 (29%) | |
TRG4 | 19 (26%) | 16 (29%) | 3 (18%) | |
TRG5 | 9 (13%) | 7 (13%) | 2 (12%) | |
Unknown | 10 | 7 | 3 | |
TNM | >0.9 | |||
T0-TisN0M0 | 3 (3.7%) | 3 (4.8%) | 0 (0%) | |
T1-T2N0 | 25 (30%) | 16 (26%) | 9 (45%) | |
T2-T3N0-N1 | 21 (26%) | 17 (27%) | 4 (20%) | |
T2N1-N2 | 4 (4.9%) | 2 (3.2%) | 2 (10%) | |
T3N2-N3 | 9 (11%) | 8 (13%) | 1 (5.0%) | |
T4N0-N1 | 12 (15%) | 9 (15%) | 3 (15%) | |
T4N2-N3 | 8 (9.8%) | 7 (11%) | 1 (5.0%) | |
Stage | >0.9 | |||
0 | 3 (3.7%) | 3 (4.8%) | 0 (0%) | |
I | 25 (30%) | 16 (26%) | 9 (45%) | |
II | 33 (40%) | 26 (42%) | 7 (35%) | |
III | 21 (26%) | 17 (27%) | 4 (20%) | |
Grade | >0.9 | |||
G1 | 14 (26%) | 11 (28%) | 3 (21%) | |
G2 | 27 (50%) | 21 (53%) | 6 (43%) | |
G3 | 13 (24%) | 8 (20%) | 5 (36%) | |
Unknown | 28 | 22 | 6 | |
Lauren.Class | >0.9 | |||
8 (9.8%) | 5 (8.1%) | 3 (15%) | ||
Diffuse | 21 (26%) | 18 (29%) | 3 (15%) | |
Intestinal | 45 (55%) | 34 (55%) | 11 (55%) | |
Mixed | 8 (9.8%) | 5 (8.1%) | 3 (15%) | |
Margins | 0.7 | |||
R0 | 78 (95%) | 61 (98%) | 17 (85%) | |
R1 | 4 (4.9%) | 1 (1.6%) | 3 (15%) | |
ACT | >0.9 | |||
Adjuvant Treatment | 68 (83%) | 53 (85%) | 15 (75%) | |
Observation | 14 (17%) | 9 (15%) | 5 (25%) | |
ACT.Regimen | 0.2 | |||
19 (23%) | 9 (15%) | 10 (50%) | ||
Chemoimmunotherapy | 4 (4.9%) | 4 (6.5%) | 0 (0%) | |
Chemotherapy | 58 (71%) | 48 (77%) | 10 (50%) | |
Immunotherapy | 1 (1.2%) | 1 (1.6%) | 0 (0%) | |
DFS.Event | >0.9 | |||
Recurrence | 37 (45%) | 29 (47%) | 8 (40%) | |
No Recurrence | 45 (55%) | 33 (53%) | 12 (60%) | |
OS.Event | >0.9 | |||
Deceased | 25 (30%) | 19 (31%) | 6 (30%) | |
Alive | 57 (70%) | 43 (69%) | 14 (70%) | |
FU.months | 32 (2 - 93) | 29 (2 - 93) | 34 (7 - 68) | >0.9 |
1Median (Min - Max); n (%) | ||||
2Wilcoxon rank sum test; Fisher's exact test | ||||
save_as_docx(fit1, path= "~/Downloads/merged_table.docx")#RFS by inclusion status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~Included, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
Included, data = circ_data)
n events median 0.95LCL 0.95UCL
Included=FALSE 20 8 52.0 31.4 NA
Included=TRUE 62 29 42.1 18.6 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ Included, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - Inclusion status", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("Included", "Excluded"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ Included, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
Included=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 12.000 5.000 0.718 0.107 0.449 0.872
Included=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 26.0000 27.0000 0.5396 0.0661 0.4024 0.6582 circ_data$Included <- factor(circ_data$Included, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ Included, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ Included, data = circ_data)
n= 82, number of events= 37
coef exp(coef) se(coef) z Pr(>|z|)
IncludedFALSE -0.2622 0.7693 0.3998 -0.656 0.512
exp(coef) exp(-coef) lower .95 upper .95
IncludedFALSE 0.7693 1.3 0.3514 1.684
Concordance= 0.546 (se = 0.031 )
Likelihood ratio test= 0.45 on 1 df, p=0.5
Wald test = 0.43 on 1 df, p=0.5
Score (logrank) test = 0.43 on 1 df, p=0.5cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 0.77 (0.35-1.68); p = 0.512"#OS by inclusion status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~Included, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
Included, data = circ_data)
n events median 0.95LCL 0.95UCL
Included=FALSE 20 6 NA 37.4 NA
Included=TRUE 62 19 NA 52.7 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ Included, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - Inclusion status", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("Included", "Excluded"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ Included, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
Included=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 15.0000 3.0000 0.8382 0.0858 0.5770 0.9450
Included=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 34.0000 14.0000 0.7568 0.0572 0.6225 0.8489 circ_data$Included <- factor(circ_data$Included, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ Included, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ Included, data = circ_data)
n= 82, number of events= 25
coef exp(coef) se(coef) z Pr(>|z|)
IncludedFALSE -0.09663 0.90789 0.46937 -0.206 0.837
exp(coef) exp(-coef) lower .95 upper .95
IncludedFALSE 0.9079 1.101 0.3618 2.278
Concordance= 0.524 (se = 0.041 )
Likelihood ratio test= 0.04 on 1 df, p=0.8
Wald test = 0.04 on 1 df, p=0.8
Score (logrank) test = 0.04 on 1 df, p=0.8cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 0.91 (0.36-2.28); p = 0.837"#Heatmap with Clinical & Genomics Factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)
ha <- HeatmapAnnotation(
Stage = circ_data$Stage,
Gender = circ_data$Gender,
PrimSite = circ_data$PrimSite,
NAC = circ_data$NAC,
ACT = circ_data$ACT,
ctDNA.Base = circ_data$ctDNA.Base,
ctDNA.C2D1 = circ_data$ctDNA.C2D1,
ctDNA.postNAC = circ_data$ctDNA.postNAC,
ctDNA.MRD = circ_data$ctDNA.MRD,
ctDNA.surveillance = circ_data$ctDNA.surveillance,
DFS.Event = circ_data$DFS.Event,
OS.Event = circ_data$OS.Event,
col = list(Stage = c("0" = "seagreen1", "I" = "seagreen1", "II" = "orange", "III" = "purple"),
Gender = c("Female" = "goldenrod" , "Male" = "blue4"),
PrimSite = c("Stomach" = "brown", "G/J" = "darkgreen", "Oesophagus" = "orange4"),
NAC = c("FALSE" = "cornflowerblue", "TRUE" ="darkmagenta"),
ACT = c("TRUE" = "brown4", "FALSE" ="khaki"),
ctDNA.Base = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.C2D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.postNAC = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
ctDNA.surveillance = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
DFS.Event = c("TRUE" = "red3", "FALSE" ="blue"),
OS.Event = c("TRUE" = "black", "FALSE" ="gray")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()#Overview Plot
setwd("~/Downloads")
clinstage<- read.csv("PLAGAST_OP.csv")
clinstage_df<- as.data.frame(clinstage)
#Display the swimmer plot with the label box
oplot<-swimmer_plot(df=clinstage_df,
id='PatientName',
end='fu.diff.months',
fill='gray',
width=.01,)
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(-12, 96, by = 6))
oplot <- oplot + labs(x ="Patients" , y="Months from Surgery")
oplot
##plot events
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
id='PatientName',
time='date.diff.months',
name_shape ='Event_type',
name_col = 'Event',
size=3.5,fill='black',
#col='darkgreen'
)
oplot_ev1
#Shape customization to Event_type
oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",values=c(1,16,6,18,4),breaks=c('ctDNA_neg','ctDNA_pos','Imaging','Surgery','Death'))
oplot_ev1.1
#plot treatment
oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
id='PatientName',
start='Tx_start.months',
end='Tx_end.months',
name_col='Tx_type',
size=3.5,
name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2 
#colour customization
oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "purple","black","black", "lightblue", "green", "red", "blue","orange"))
oplot_ev2.2
#RFS by ctDNA at Baseline - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Base, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Base, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Base=NEGATIVE 17 5 NA 42.1 NA
ctDNA.Base=POSITIVE 39 20 22.3 13.3 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA Baseline | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Base, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Base=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 11.000 4.000 0.756 0.106 0.473 0.901
ctDNA.Base=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 13.0000 19.0000 0.4772 0.0849 0.3057 0.6301 circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Base, data = circ_data)
n= 56, number of events= 25
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.BasePOSITIVE 0.9196 2.5082 0.5030 1.828 0.0675 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.BasePOSITIVE 2.508 0.3987 0.9359 6.722
Concordance= 0.604 (se = 0.041 )
Likelihood ratio test= 3.92 on 1 df, p=0.05
Wald test = 3.34 on 1 df, p=0.07
Score (logrank) test = 3.58 on 1 df, p=0.06cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.51 (0.94-6.72); p = 0.068"#OS by ctDNA at Baseline - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Base, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Base, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Base=NEGATIVE 17 4 NA 52.7 NA
ctDNA.Base=POSITIVE 39 13 NA 24.5 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Baseline | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Base, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Base=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 14.0000 1.0000 0.9412 0.0571 0.6502 0.9915
ctDNA.Base=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 17.0000 11.0000 0.6832 0.0809 0.4963 0.8129 circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Base, data = circ_data)
n= 56, number of events= 17
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.BasePOSITIVE 0.7361 2.0878 0.5781 1.273 0.203
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.BasePOSITIVE 2.088 0.479 0.6724 6.483
Concordance= 0.597 (se = 0.05 )
Likelihood ratio test= 1.81 on 1 df, p=0.2
Wald test = 1.62 on 1 df, p=0.2
Score (logrank) test = 1.69 on 1 df, p=0.2cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.09 (0.67-6.48); p = 0.203"#RFS by ctDNA levels at Baseline based on AUC optimal MTM/ml level - ctDNA Baseline positive
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)
#DFS.Event
circ_data <- circ_data[complete.cases(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM),]
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
ROC <- roc(DFS.Event ~ ctDNA.Base.MTM, data = circ_data, ci = TRUE)Setting levels: control = FALSE, case = TRUE
Setting direction: controls < casesp<-ggroc(ROC,
aes = c("linetype"), color = "blue", size = 1,
legacy.axes = TRUE) +
geom_abline(color = "dark grey", size = 0.5) +
theme_classic()+
ylab("Sensitivity") + theme(axis.title.x = element_text(color="black", size=14), axis.title.y = element_text(color="black", size=14),axis.text.x = element_text(colour = "black", size=14),axis.text.y = element_text(colour = "black",size=14),legend.title = element_blank(),legend.text = element_text(size=14))Warning: Ignoring unknown parameters: `aes`Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.p
#AUC
AUC <- auc(ROC)
print(AUC)Area under the curve: 0.6211AUC_conf <- ci.auc(ROC)
print(AUC_conf)95% CI: 0.4354-0.8067 (DeLong)res.roc <- roc(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM)Setting levels: control = FALSE, case = TRUE
Setting direction: controls < casesplot.roc(res.roc, print.auc = TRUE, print.thres = "best")
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)
# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.6mMTM.Q = case_when(
ctDNA.Base.MTM < 1.085 ~ 1,
ctDNA.Base.MTM >= 1.085 ~ 2
))
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.6mMTM.Q, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.6mMTM.Q, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.6mMTM.Q=1 16 5 NA 26.32 NA
ctDNA.6mMTM.Q=2 23 15 14.5 7.79 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA MTM/mL groups at Baseline", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<1.085", "MTM/mL≥1.085"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.6mMTM.Q=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 8.000 4.000 0.731 0.117 0.427 0.891
ctDNA.6mMTM.Q=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 5.000 15.000 0.309 0.103 0.129 0.510 circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<1.085", "MTM/mL≥1.085"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data)
n= 39, number of events= 20
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.6mMTM.QMTM/mL≥1.085 1.0492 2.8553 0.5217 2.011 0.0443 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.6mMTM.QMTM/mL≥1.085 2.855 0.3502 1.027 7.939
Concordance= 0.617 (se = 0.056 )
Likelihood ratio test= 4.64 on 1 df, p=0.03
Wald test = 4.04 on 1 df, p=0.04
Score (logrank) test = 4.4 on 1 df, p=0.04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.86 (1.03-7.94); p = 0.044"#OS by ctDNA levels at Baseline based on AUC optimal MTM/mL level from RFS model - ctDNA Baseline positive
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)
# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA
circ_data <- circ_data %>%
mutate(ctDNA.6mMTM.Q = case_when(
ctDNA.Base.MTM < 1.085 ~ 1,
ctDNA.Base.MTM >= 1.085 ~ 2
))
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.6mMTM.Q, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.6mMTM.Q, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.6mMTM.Q=1 16 3 NA NA NA
ctDNA.6mMTM.Q=2 23 10 38 18.9 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MTM/mL groups at Baseline", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<1.085", "MTM/mL≥1.085"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.6mMTM.Q=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 9.000 3.000 0.804 0.102 0.506 0.932
ctDNA.6mMTM.Q=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 8.000 8.000 0.598 0.115 0.342 0.781 circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<1.085", "MTM/mL≥1.085"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data)
n= 39, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.6mMTM.QMTM/mL≥1.085 0.9680 2.6326 0.6668 1.452 0.147
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.6mMTM.QMTM/mL≥1.085 2.633 0.3798 0.7125 9.727
Concordance= 0.566 (se = 0.076 )
Likelihood ratio test= 2.43 on 1 df, p=0.1
Wald test = 2.11 on 1 df, p=0.1
Score (logrank) test = 2.26 on 1 df, p=0.1cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.63 (0.71-9.73); p = 0.147"#RFS by ctDNA at Baseline - excluding pts that did not receive NAC
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Base, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Base, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Base=NEGATIVE 16 5 NA 42.1 NA
ctDNA.Base=POSITIVE 35 17 26.3 14.5 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA Baseline | NAC treated only", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Base, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Base=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 10.000 4.000 0.740 0.112 0.446 0.894
ctDNA.Base=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 12.0000 16.0000 0.5096 0.0893 0.3256 0.6670 circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Base, data = circ_data)
n= 51, number of events= 22
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.BasePOSITIVE 0.8020 2.2300 0.5121 1.566 0.117
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.BasePOSITIVE 2.23 0.4484 0.8173 6.085
Concordance= 0.599 (se = 0.044 )
Likelihood ratio test= 2.78 on 1 df, p=0.1
Wald test = 2.45 on 1 df, p=0.1
Score (logrank) test = 2.58 on 1 df, p=0.1cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.23 (0.82-6.08); p = 0.117"#OS by ctDNA at Baseline - excluding pts that did not receive NAC
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Base, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Base, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Base=NEGATIVE 16 4 NA 52.7 NA
ctDNA.Base=POSITIVE 35 11 NA 24.5 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Baseline | NAC treated only", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Base, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Base=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 13.0000 1.0000 0.9375 0.0605 0.6323 0.9910
ctDNA.Base=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 15.0000 9.0000 0.7040 0.0854 0.5012 0.8367 circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Base, data = circ_data)
n= 51, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.BasePOSITIVE 0.6136 1.8470 0.5897 1.041 0.298
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.BasePOSITIVE 1.847 0.5414 0.5815 5.867
Concordance= 0.59 (se = 0.055 )
Likelihood ratio test= 1.17 on 1 df, p=0.3
Wald test = 1.08 on 1 df, p=0.3
Score (logrank) test = 1.12 on 1 df, p=0.3cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.85 (0.58-5.87); p = 0.298"#RFS by ctDNA levels at Baseline based on AUC optimal MTM/ml level - ctDNA Baseline positive excluding pts that did not receive NAC
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)
#DFS.Event
circ_data <- circ_data[complete.cases(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM),]
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
ROC <- roc(DFS.Event ~ ctDNA.Base.MTM, data = circ_data, ci = TRUE)Setting levels: control = FALSE, case = TRUE
Setting direction: controls < casesp<-ggroc(ROC,
aes = c("linetype"), color = "blue", size = 1,
legacy.axes = TRUE) +
geom_abline(color = "dark grey", size = 0.5) +
theme_classic()+
ylab("Sensitivity") + theme(axis.title.x = element_text(color="black", size=14), axis.title.y = element_text(color="black", size=14),axis.text.x = element_text(colour = "black", size=14),axis.text.y = element_text(colour = "black",size=14),legend.title = element_blank(),legend.text = element_text(size=14))Warning: Ignoring unknown parameters: `aes`p
#AUC
AUC <- auc(ROC)
print(AUC)Area under the curve: 0.6373AUC_conf <- ci.auc(ROC)
print(AUC_conf)95% CI: 0.4424-0.8321 (DeLong)res.roc <- roc(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM)Setting levels: control = FALSE, case = TRUE
Setting direction: controls < casesplot.roc(res.roc, print.auc = TRUE, print.thres = "best")
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)
# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.6mMTM.Q = case_when(
ctDNA.Base.MTM < 0.71 ~ 1,
ctDNA.Base.MTM >= 0.71 ~ 2
))
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.6mMTM.Q, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.6mMTM.Q, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.6mMTM.Q=1 11 2 NA NA NA
ctDNA.6mMTM.Q=2 24 15 14.5 7.79 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA MTM/mL groups at Baseline", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.71", "MTM/mL≥0.71"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.6mMTM.Q=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 6.0000 1.0000 0.9091 0.0867 0.5081 0.9867
ctDNA.6mMTM.Q=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 15.000 0.340 0.102 0.156 0.535 circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.71", "MTM/mL≥0.71"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.6mMTM.Q, data = circ_data)
n= 35, number of events= 17
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.6mMTM.QMTM/mL≥0.71 1.5274 4.6061 0.7556 2.021 0.0432 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.6mMTM.QMTM/mL≥0.71 4.606 0.2171 1.048 20.25
Concordance= 0.632 (se = 0.056 )
Likelihood ratio test= 5.83 on 1 df, p=0.02
Wald test = 4.09 on 1 df, p=0.04
Score (logrank) test = 4.92 on 1 df, p=0.03cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.61 (1.05-20.25); p = 0.043"#RFS by ctDNA on-NAT - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.C2D1!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.C2D1, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.C2D1, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C2D1=NEGATIVE 20 4 NA NA NA
ctDNA.C2D1=POSITIVE 21 14 13.3 6.57 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA status on-NAT | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.C2D1, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C2D1=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 14.0000 3.0000 0.8400 0.0853 0.5792 0.9459
ctDNA.C2D1=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 4.000 13.000 0.342 0.109 0.146 0.550 circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C2D1, data = circ_data)
n= 41, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C2D1POSITIVE 1.8205 6.1750 0.5767 3.157 0.0016 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C2D1POSITIVE 6.175 0.1619 1.994 19.12
Concordance= 0.718 (se = 0.046 )
Likelihood ratio test= 12.42 on 1 df, p=4e-04
Wald test = 9.97 on 1 df, p=0.002
Score (logrank) test = 12.64 on 1 df, p=4e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 6.17 (1.99-19.12); p = 0.002"#OS by ctDNA on-NAT - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.C2D1!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.C2D1, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.C2D1, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.C2D1=NEGATIVE 20 3 NA NA NA
ctDNA.C2D1=POSITIVE 21 9 24.5 14.4 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA status on-NAT | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.C2D1, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.C2D1=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 15.0000 1.0000 0.9500 0.0487 0.6947 0.9928
ctDNA.C2D1=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 8.000 0.586 0.114 0.335 0.771 circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.C2D1, data = circ_data)
n= 41, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.C2D1POSITIVE 1.5506 4.7145 0.6795 2.282 0.0225 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.C2D1POSITIVE 4.714 0.2121 1.245 17.86
Concordance= 0.706 (se = 0.054 )
Likelihood ratio test= 6.18 on 1 df, p=0.01
Wald test = 5.21 on 1 df, p=0.02
Score (logrank) test = 6.2 on 1 df, p=0.01cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.71 (1.24-17.86); p = 0.022"#RFS by ctDNA post-NAT - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.postNAC, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.postNAC, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.postNAC=NEGATIVE 30 10 NA 21.52 NA
ctDNA.postNAC=POSITIVE 11 8 7.79 3.55 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postNAC, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA status post-NAT | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.postNAC, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.postNAC=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 13.0000 10.0000 0.6377 0.0933 0.4269 0.7883
ctDNA.postNAC=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 8.00000 0.13636 0.12392 0.00767 0.44263 circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postNAC, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.postNAC, data = circ_data)
n= 41, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.postNACPOSITIVE 1.6603 5.2610 0.5036 3.297 0.000978 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.postNACPOSITIVE 5.261 0.1901 1.961 14.12
Concordance= 0.669 (se = 0.052 )
Likelihood ratio test= 9.69 on 1 df, p=0.002
Wald test = 10.87 on 1 df, p=0.001
Score (logrank) test = 13.08 on 1 df, p=3e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.26 (1.96-14.12); p = 0.001"#OS by ctDNA post-NAT - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.postNAC, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.postNAC, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.postNAC=NEGATIVE 30 5 NA NA NA
ctDNA.postNAC=POSITIVE 11 8 18.9 11.7 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postNAC, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA status post-NAT | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.postNAC, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.postNAC=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 16.0000 3.0000 0.9000 0.0548 0.7212 0.9666
ctDNA.postNAC=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 2.0000 7.0000 0.2273 0.1409 0.0346 0.5207 circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postNAC, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.postNAC, data = circ_data)
n= 41, number of events= 13
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.postNACPOSITIVE 1.9944 7.3479 0.5811 3.432 0.000599 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.postNACPOSITIVE 7.348 0.1361 2.352 22.95
Concordance= 0.711 (se = 0.07 )
Likelihood ratio test= 11.82 on 1 df, p=6e-04
Wald test = 11.78 on 1 df, p=6e-04
Score (logrank) test = 15.82 on 1 df, p=7e-05cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.35 (2.35-22.95); p = 0.001"#Multivariate cox regression for RFS - ctDNA post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("Stomach","G/J"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"), labels = c("Yes", "No"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.postNAC + PrimSite + Gender + Age.Group + ACT, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for RFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#Multivariate cox regression for OS - ctDNA post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("Stomach","G/J"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"), labels = c("Yes", "No"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.postNAC + PrimSite + Gender + Age.Group + ACT, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#Association of ctDNA Dynamics post-NAT and Response
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
#Vertical Fisher plot for ctDNA clearance post-NAT and Rec Status
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
40 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=ctDNA +/-/- 5 1 NA 18.20 NA
ctDNA.Dynamics=ctDNA +/+/- 10 5 14.55 7.36 NA
ctDNA.Dynamics=ctDNA +/+/+ 7 6 6.21 2.04 NAcirc_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-", "ctDNA +/+/-", "ctDNA +/+/+"))
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$TRG)
fisher_exact_test <- fisher.test(contingency_table)
chi_square_test <- chisq.test(contingency_table)Warning: Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 11.378, df = 2, p-value = 0.003383print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.002557
alternative hypothesis: two.sidedprint(contingency_table)
TRG1/2/3 TRG4/5
ctDNA +/-/- 4 0
ctDNA +/+/- 6 4
ctDNA +/+/+ 0 7table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
# Swapping x and y in ggplot function to make bar plot vertical
ggplot(table_df, aes(y = Var1, x = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(x = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA Dymamics post-NAT", y = "ctDNA", x = "Patients (%)", fill = "Path Response") +
scale_x_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("TRG1/2/3" = "lightblue3", "TRG4/5" = "red")) + # define custom colors
theme(axis.text.y = element_text(angle = 0, hjust = 1.5, size = 14), # increase y-axis text size
axis.text.x = element_text(size = 14, color = "black"), # increase x-axis text size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculating p-value with Fisher exact test for the ctDNA +/+/+ vs ctDNA +/+/-
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
#Vertical Fisher plot for ctDNA clearance post-NAT and Rec Status
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="ctDNA +/-/-",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
40 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=ctDNA +/+/- 10 5 14.55 7.36 NA
ctDNA.Dynamics=ctDNA +/+/+ 7 6 6.21 2.04 NAcirc_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/+/-", "ctDNA +/+/+"))
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$TRG)
fisher_exact_test <- fisher.test(contingency_table)
chi_square_test <- chisq.test(contingency_table)Warning: Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 4.1295, df = 1, p-value = 0.04214print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0345
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.176016 Inf
sample estimates:
odds ratio
Inf print(contingency_table)
TRG1/2/3 TRG4/5
ctDNA +/+/- 6 4
ctDNA +/+/+ 0 7#RFS by ctDNA Dynamics post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=ctDNA +/-/- 5 1 NA 18.20 NA
ctDNA.Dynamics=ctDNA +/+/- 10 5 14.55 7.36 NA
ctDNA.Dynamics=ctDNA +/+/+ 7 6 6.21 2.04 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA Dynamics post-NAT", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=ctDNA +/-/-
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1.000 0.000 1.0000 1.000
24 2 1 0.667 0.272 0.0541 0.945
ctDNA.Dynamics=ctDNA +/+/-
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1.00 0.000 1.000 1.000
24 2 5 0.48 0.164 0.161 0.745
ctDNA.Dynamics=ctDNA +/+/+
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 7 0 1 0 1 1 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 22, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsctDNA +/+/- 1.343 3.831 1.100 1.221 0.2219
ctDNA.DynamicsctDNA +/+/+ 2.921 18.568 1.157 2.526 0.0116 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsctDNA +/+/- 3.831 0.26100 0.4439 33.07
ctDNA.DynamicsctDNA +/+/+ 18.568 0.05386 1.9237 179.22
Concordance= 0.756 (se = 0.053 )
Likelihood ratio test= 10.01 on 2 df, p=0.007
Wald test = 8.49 on 2 df, p=0.01
Score (logrank) test = 11.47 on 2 df, p=0.003#OS by ctDNA Dynamics post-NAT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=ctDNA +/-/- 5 0 NA NA NA
ctDNA.Dynamics=ctDNA +/+/- 10 2 NA NA NA
ctDNA.Dynamics=ctDNA +/+/+ 7 6 14.4 8.54 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA Dynamics post-NAT", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=ctDNA +/-/-
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1
24 2 0 1 0 NA NA
ctDNA.Dynamics=ctDNA +/+/-
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1.0 0.000 1.000 1.000
24 2 2 0.8 0.126 0.409 0.946
ctDNA.Dynamics=ctDNA +/+/+
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 7 0 1.000 0.000 1.00000 1.000
24 1 5 0.171 0.156 0.00794 0.526circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
cox_fit <- coxphf(surv_object ~ ctDNA.Dynamics, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.DynamicsctDNA +/+/- 1.178268 1.696304 3.248742 0.2627946 448.914 0.7281482 0.393484095
ctDNA.DynamicsctDNA +/+/+ 2.776185 1.609915 16.057642 1.8668569 2102.174 7.2774206 0.006982673
Likelihood ratio test=8.847995 on 2 df, p=0.01198622, n=22
Wald test = 5.979249 on 2 df, p = 0.05030632
Covariance-Matrix:
ctDNA.DynamicsctDNA +/+/- ctDNA.DynamicsctDNA +/+/+
ctDNA.DynamicsctDNA +/+/- 2.877449 2.403504
ctDNA.DynamicsctDNA +/+/+ 2.403504 2.591825#Levels of Baseline MTM/mL in ctDNA Dynamics post-NAT categories
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
))
# Transform ctDNA.Base.MTM with log10
circ_data <- subset(circ_data, !is.na(ctDNA.Dynamics))
circ_data$ctDNA.Base.MTM <- as.numeric(as.character(circ_data$ctDNA.Base.MTM))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
median_Base_MTM <- aggregate(ctDNA.Base.MTM ~ ctDNA.Dynamics, data = circ_data, FUN = median)
print(median_Base_MTM)
# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=ctDNA.Dynamics, y=ctDNA.Base.MTM, fill=ctDNA.Dynamics)) +
geom_violin(trim=FALSE) +
scale_fill_manual(values=c("ctDNA +/-/-"="lightblue", "ctDNA +/+/-"="lightgreen", "ctDNA +/+/+"="salmon")) +
geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
labs(title="Baseline MTM/mL | Dynamics post-NAT", x="Dynamics post-NAT", y="Baseline MTM/mL") +
theme_minimal() +
theme(legend.position="none")
m3_1v2 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/-/-", "ctDNA +/+/-"), ],
na.rm = TRUE)Warning: cannot compute exact p-value with tiesprint(m3_1v2)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by ctDNA.Dynamics
W = 26, p-value = 0.9511
alternative hypothesis: true location shift is not equal to 0m3_1v3 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/-/-", "ctDNA +/+/+"), ],
na.rm = TRUE)
print(m3_1v3)
Wilcoxon rank sum exact test
data: ctDNA.Base.MTM by ctDNA.Dynamics
W = 7, p-value = 0.1061
alternative hypothesis: true location shift is not equal to 0m3_2v3 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/+/-", "ctDNA +/+/+"), ],
na.rm = TRUE)Warning: cannot compute exact p-value with tiesprint(m3_2v3)
Wilcoxon rank sum test with continuity correction
data: ctDNA.Base.MTM by ctDNA.Dynamics
W = 2, p-value = 0.001506
alternative hypothesis: true location shift is not equal to 0#Time of ctDNA draws at the MRD window from surgery
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE, ]
circ_data$ctDNA.MRD.Date <- as.Date(circ_data$ctDNA.MRD.Date, format="%m/%d/%Y")
circ_data$Surgery.Date <- as.Date(circ_data$Surgery.Date, format="%m/%d/%Y")
circ_data$Time_to_ctDNA_MRD <- as.numeric(difftime(circ_data$ctDNA.MRD.Date, circ_data$Surgery.Date, units="days"))
median_ctDNA_MRD <- median(circ_data$Time_to_ctDNA_MRD, na.rm=TRUE)
range_ctDNA_MRD <- range(circ_data$Time_to_ctDNA_MRD, na.rm=TRUE)
Q1_ctDNA_MRD <- quantile(circ_data$Time_to_ctDNA_MRD, 0.25, na.rm=TRUE)
Q3_ctDNA_MRD <- quantile(circ_data$Time_to_ctDNA_MRD, 0.75, na.rm=TRUE)
IQR_ctDNA_MRD <- IQR(circ_data$Time_to_ctDNA_MRD, na.rm=TRUE)
cat("Median time from surgery to ctDNA MRD time point:", median_ctDNA_MRD, "days\n")Median time from surgery to ctDNA MRD time point: 40.5 dayscat("Range:", range_ctDNA_MRD[1], "-", range_ctDNA_MRD[2], "days\n")Range: 15 - 82 dayscat("Q1 (25th percentile):", Q1_ctDNA_MRD, "days\n")Q1 (25th percentile): 30.25 dayscat("Q3 (75th percentile):", Q3_ctDNA_MRD, "days\n")Q3 (75th percentile): 47 dayscat("Interquartile Range (IQR):", IQR_ctDNA_MRD, "days\n")Interquartile Range (IQR): 16.75 days#RFS by ctDNA at the MRD Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 40 14 NA 23.32 NA
ctDNA.MRD=POSITIVE 7 7 3.57 3.21 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA MRD window | All pts", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 40 0 1.000 0.0000 1.000 1.000
24 18 13 0.628 0.0836 0.442 0.766
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 7 0 1 0 1 1 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 47, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.5604 12.9415 0.5704 4.489 7.17e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 12.94 0.07727 4.231 39.59
Concordance= 0.666 (se = 0.047 )
Likelihood ratio test= 17.26 on 1 df, p=3e-05
Wald test = 20.15 on 1 df, p=7e-06
Score (logrank) test = 31.36 on 1 df, p=2e-08cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 12.94 (4.23-39.59); p = 0"#OS by ctDNA at the MRD Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 40 6 NA NA NA
ctDNA.MRD=POSITIVE 10 8 8.59 5.31 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 40 0 1.000 0.0000 1.000 1.000
24 23 5 0.849 0.0632 0.671 0.935
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1 0 1 1 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 50, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.6769 14.5406 0.5942 4.505 6.64e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 14.54 0.06877 4.537 46.6
Concordance= 0.771 (se = 0.055 )
Likelihood ratio test= 19.56 on 1 df, p=1e-05
Wald test = 20.29 on 1 df, p=7e-06
Score (logrank) test = 33.05 on 1 df, p=9e-09cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 14.54 (4.54-46.6); p = 0"#RFS by ctDNA at the MRD Window - ACT pts only
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 39 14 NA 23.32 NA
ctDNA.MRD=POSITIVE 6 6 3.54 3.21 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA MRD window | ACT treated", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 39 0 1.000 0.000 1.000 1.000
24 18 13 0.623 0.084 0.437 0.763
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 6 0 1 0 1 1 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 45, number of events= 20
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.4748 11.8797 0.5874 4.213 2.52e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 11.88 0.08418 3.757 37.57
Concordance= 0.651 (se = 0.048 )
Likelihood ratio test= 14.56 on 1 df, p=1e-04
Wald test = 17.75 on 1 df, p=3e-05
Score (logrank) test = 27.27 on 1 df, p=2e-07cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 11.88 (3.76-37.57); p = 0"#OS by ctDNA at the MRD Window - ACT pts only
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 39 6 NA NA NA
ctDNA.MRD=POSITIVE 7 5 10.3 5.54 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | ACT treated", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 39 0 1.000 0.0000 1.000 1.000
24 23 5 0.847 0.0636 0.669 0.934
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 7 0 1 0 1 1 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 46, number of events= 11
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.3872 10.8835 0.6522 3.66 0.000252 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 10.88 0.09188 3.031 39.08
Concordance= 0.717 (se = 0.071 )
Likelihood ratio test= 11.36 on 1 df, p=8e-04
Wald test = 13.4 on 1 df, p=3e-04
Score (logrank) test = 20.32 on 1 df, p=7e-06cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 10.88 (3.03-39.08); p = 0"#RFS by ctDNA at the MRD Window - NAC pts only
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 35 12 NA 23.32 NA
ctDNA.MRD=POSITIVE 5 5 4.36 3.57 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA MRD window | NAC treated", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 35 0 1.00 0.0000 1.000 1.000
24 15 11 0.64 0.0892 0.439 0.785
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 40, number of events= 17
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.2339 9.3362 0.6137 3.64 0.000272 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.336 0.1071 2.804 31.08
Concordance= 0.639 (se = 0.051 )
Likelihood ratio test= 10.63 on 1 df, p=0.001
Wald test = 13.25 on 1 df, p=3e-04
Score (logrank) test = 18.98 on 1 df, p=1e-05cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.34 (2.8-31.08); p = 0"#OS by ctDNA at the MRD Window - NAC pts only
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 35 6 NA NA NA
ctDNA.MRD=POSITIVE 8 6 7.1 5.31 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | NAC treated", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 35 0 1.000 0.0000 1.000 1.000
24 18 5 0.821 0.0741 0.616 0.922
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1 0 1 1 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 43, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.3851 10.8601 0.6224 3.832 0.000127 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 10.86 0.09208 3.206 36.78
Concordance= 0.742 (se = 0.065 )
Likelihood ratio test= 13.1 on 1 df, p=3e-04
Wald test = 14.68 on 1 df, p=1e-04
Score (logrank) test = 22.15 on 1 df, p=3e-06cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 10.86 (3.21-36.78); p = 0"#RFS by ctDNA at the MRD Window & ypT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 31 8 NA NA NA
ctDNA.Stage.II.TNM=2 4 4 5.10 3.21 NA
ctDNA.Stage.II.TNM=3 9 6 19.34 11.55 NA
ctDNA.Stage.II.TNM=4 3 3 3.51 1.70 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="RFS - ctDNA MRD & ypTN", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.000 1.000 1.000
24 15 8 0.698 0.091 0.481 0.838
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 4 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.0 0.000 1.0000 1.000
24 3 5 0.4 0.174 0.0981 0.697
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 47, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 2.6818 14.6110 0.6876 3.900 9.62e-05 ***
ctDNA.Stage.II.TNMctDNA(-) & T4 1.0893 2.9722 0.5407 2.015 0.0439 *
ctDNA.Stage.II.TNMctDNA(+) & T4 3.4934 32.8977 0.8353 4.182 2.89e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 14.611 0.06844 3.796 56.233
ctDNA.Stage.II.TNMctDNA(-) & T4 2.972 0.33645 1.030 8.576
ctDNA.Stage.II.TNMctDNA(+) & T4 32.898 0.03040 6.399 169.126
Concordance= 0.731 (se = 0.051 )
Likelihood ratio test= 21.92 on 3 df, p=7e-05
Wald test = 22.7 on 3 df, p=5e-05
Score (logrank) test = 37.96 on 3 df, p=3e-08#Repeat analysis to compare ctDNA MRD (-) vs (+) in T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 31 8 NA NA NA
ctDNA.Stage.II.TNM=2 4 4 5.10 3.21 NA
ctDNA.Stage.II.TNM=3 9 6 19.34 11.55 NA
ctDNA.Stage.II.TNM=4 3 3 3.51 1.70 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="RFS - ctDNA MRD & ypTN", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.000 1.000 1.000
24 15 8 0.698 0.091 0.481 0.838
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 4 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.0 0.000 1.0000 1.000
24 3 5 0.4 0.174 0.0981 0.697
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 47, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNM4 0.81163 2.25158 0.80842 1.004 0.3154
ctDNA.Stage.II.TNM1 -2.68177 0.06844 0.68763 -3.900 9.62e-05 ***
ctDNA.Stage.II.TNM3 -1.59246 0.20342 0.71596 -2.224 0.0261 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNM4 2.25158 0.4441 0.46170 10.9804
ctDNA.Stage.II.TNM1 0.06844 14.6110 0.01778 0.2634
ctDNA.Stage.II.TNM3 0.20342 4.9158 0.05000 0.8276
Concordance= 0.731 (se = 0.051 )
Likelihood ratio test= 21.92 on 3 df, p=7e-05
Wald test = 22.7 on 3 df, p=5e-05
Score (logrank) test = 37.96 on 3 df, p=3e-08#OS by ctDNA at the MRD Window & ypT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 31 3 NA NA NA
ctDNA.Stage.II.TNM=2 5 3 21.48 8.66 NA
ctDNA.Stage.II.TNM=3 9 3 49.70 49.70 NA
ctDNA.Stage.II.TNM=4 5 5 5.31 0.58 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypTN", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.0000 1.000 1.000
24 18 3 0.875 0.0681 0.658 0.959
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.000 0.000 1.000 1.000
24 5 2 0.762 0.148 0.332 0.935
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 50, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 2.5315 12.5725 0.8498 2.979 0.00289 **
ctDNA.Stage.II.TNMctDNA(-) & T4 1.2740 3.5751 0.8169 1.560 0.11886
ctDNA.Stage.II.TNMctDNA(+) & T4 4.5082 90.7580 0.9252 4.873 1.1e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & T1-T3 12.573 0.07954 2.377 66.49
ctDNA.Stage.II.TNMctDNA(-) & T4 3.575 0.27971 0.721 17.73
ctDNA.Stage.II.TNMctDNA(+) & T4 90.758 0.01102 14.804 556.40
Concordance= 0.829 (se = 0.056 )
Likelihood ratio test= 27.86 on 3 df, p=4e-06
Wald test = 24.63 on 3 df, p=2e-05
Score (logrank) test = 59.15 on 3 df, p=9e-13#Repeat analysis to compare ctDNA post-NAT (-) vs (+) in T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 31 3 NA NA NA
ctDNA.Stage.II.TNM=2 5 3 21.48 8.66 NA
ctDNA.Stage.II.TNM=3 9 3 49.70 49.70 NA
ctDNA.Stage.II.TNM=4 5 5 5.31 0.58 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypTN", ylab= "Overall Survival Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 31 0 1.000 0.0000 1.000 1.000
24 18 3 0.875 0.0681 0.658 0.959
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 9 0 1.000 0.000 1.000 1.000
24 5 2 0.762 0.148 0.332 0.935
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 50, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNM4 1.97669 7.21877 0.84482 2.340 0.01930 *
ctDNA.Stage.II.TNM1 -2.53151 0.07954 0.84981 -2.979 0.00289 **
ctDNA.Stage.II.TNM3 -1.25752 0.28436 0.84413 -1.490 0.13629
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNM4 7.21877 0.1385 1.37833 37.8071
ctDNA.Stage.II.TNM1 0.07954 12.5725 0.01504 0.4207
ctDNA.Stage.II.TNM3 0.28436 3.5167 0.05437 1.4873
Concordance= 0.829 (se = 0.056 )
Likelihood ratio test= 27.86 on 3 df, p=4e-06
Wald test = 24.63 on 3 df, p=2e-05
Score (logrank) test = 59.15 on 3 df, p=9e-13#RFS by ctDNA at the MRD Window & ypN
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & ypNStatus == "N0" ~ 1,
ctDNA.MRD == "POSITIVE" & ypNStatus == "N0" ~ 2,
ctDNA.MRD == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
ctDNA.MRD == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 24 5 NA NA NA
ctDNA.Stage.II.TNM=2 1 1 1.70 NA NA
ctDNA.Stage.II.TNM=3 16 9 15.20 10.34 NA
ctDNA.Stage.II.TNM=4 6 6 3.97 3.51 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="RFS - ctDNA MRD & ypN", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 24 0 1.000 0.0000 1.000 1.00
24 17 4 0.823 0.0807 0.593 0.93
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 1 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 16 0 1.000 0.000 1.00000 1.000
24 1 9 0.148 0.132 0.00842 0.466
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 6 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 47, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & ypN0 5.9532 384.9884 1.5428 3.859 0.000114 ***
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 2.0297 7.6117 0.6062 3.348 0.000814 ***
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 3.6876 39.9503 0.7639 4.827 1.38e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & ypN0 384.988 0.002597 18.715 7919.53
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 7.612 0.131377 2.320 24.97
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 39.950 0.025031 8.939 178.56
Concordance= 0.792 (se = 0.048 )
Likelihood ratio test= 31.15 on 3 df, p=8e-07
Wald test = 27.95 on 3 df, p=4e-06
Score (logrank) test = 54.44 on 3 df, p=9e-12#OS by ctDNA at the MRD Window & ypN
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Stage.II.TNM = case_when(
ctDNA.MRD == "NEGATIVE" & ypNStatus == "N0" ~ 1,
ctDNA.MRD == "POSITIVE" & ypNStatus == "N0" ~ 2,
ctDNA.MRD == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
ctDNA.MRD == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
))
circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.Stage.II.TNM, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Stage.II.TNM=1 24 2 NA NA NA
ctDNA.Stage.II.TNM=2 2 2 4.46 0.38 NA
ctDNA.Stage.II.TNM=3 16 4 NA 21.25 NA
ctDNA.Stage.II.TNM=4 8 6 9.46 5.54 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypN", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Stage.II.TNM=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 24 0 1.000 0.0000 1.000 1.000
24 20 1 0.957 0.0425 0.729 0.994
ctDNA.Stage.II.TNM=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 2 0 1 0 1 1
ctDNA.Stage.II.TNM=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 16 0 1.000 0.00 1.000 1.000
24 3 4 0.565 0.19 0.165 0.835
ctDNA.Stage.II.TNM=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 8 0 1 0 1 1 circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Stage.II.TNM, data = circ_data)
n= 50, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Stage.II.TNMctDNA(+) & ypN0 5.0035 148.9381 1.1735 4.264 2.01e-05 ***
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 1.8810 6.5597 0.9016 2.086 0.037 *
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 3.5092 33.4225 0.8993 3.902 9.53e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Stage.II.TNMctDNA(+) & ypN0 148.94 0.006714 14.932 1485.5
ctDNA.Stage.II.TNMctDNA(-) & ypN+ 6.56 0.152445 1.120 38.4
ctDNA.Stage.II.TNMctDNA(+) & ypN+ 33.42 0.029920 5.736 194.8
Concordance= 0.86 (se = 0.04 )
Likelihood ratio test= 26.4 on 3 df, p=8e-06
Wald test = 23 on 3 df, p=4e-05
Score (logrank) test = 46.69 on 3 df, p=4e-10#RFS by ctDNA at the MRD Window & TRG combination (TRG1/2/3 vs TRG3-5)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
circ_data$ctDNA.pCR <- NA
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.MRD == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
ctDNA.MRD == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
ctDNA.MRD == "POSITIVE" & TRG == "TRG4/5" ~ "3"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.pCR, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.pCR=1 23 6 NA 39.09 NA
ctDNA.pCR=2 12 6 15.60 11.55 NA
ctDNA.pCR=3 3 3 4.36 3.21 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA MRD/TRG", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.pCR, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.pCR=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 23 0 1.000 0.0000 1.000 1.000
24 12 5 0.749 0.0993 0.492 0.889
ctDNA.pCR=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 12 0 1.000 0.000 1.000 1.000
24 4 6 0.417 0.157 0.131 0.686
ctDNA.pCR=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 3 0 1 0 1 1 circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.pCR, data = circ_data)
n= 38, number of events= 15
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.pCRTRG4/5 ctDNA (-) 0.9323 2.5404 0.5794 1.609 0.107610
ctDNA.pCRTRG4/5 ctDNA (+) 3.0190 20.4704 0.8850 3.411 0.000647 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.pCRTRG4/5 ctDNA (-) 2.54 0.39364 0.816 7.909
ctDNA.pCRTRG4/5 ctDNA (+) 20.47 0.04885 3.612 116.004
Concordance= 0.7 (se = 0.064 )
Likelihood ratio test= 10.66 on 2 df, p=0.005
Wald test = 11.83 on 2 df, p=0.003
Score (logrank) test = 19.3 on 2 df, p=6e-05#OS by ctDNA at the MRD Window & TRG combination (TRG1/2/3 vs TRG3-5)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
circ_data$ctDNA.pCR <- NA
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.MRD == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
ctDNA.MRD == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
ctDNA.MRD == "POSITIVE" & TRG == "TRG4/5" ~ "3"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.pCR, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.pCR=1 23 3 NA 49.70 NA
ctDNA.pCR=2 12 3 NA 16.09 NA
ctDNA.pCR=3 6 6 5.42 0.58 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA MRD/TRG", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))Call: survfit(formula = surv_object ~ ctDNA.pCR, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.pCR=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 23 0 1.000 0.0000 1.000 1.000
24 13 2 0.886 0.0776 0.607 0.971
ctDNA.pCR=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 12 0 1.0 0.000 1.000 1.000
24 6 3 0.7 0.145 0.329 0.892
ctDNA.pCR=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 6 0 1 0 1 1 circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.pCR, data = circ_data)
n= 41, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.pCRTRG4/5 ctDNA (-) 0.6450 1.9061 0.8202 0.786 0.432
ctDNA.pCRTRG4/5 ctDNA (+) 3.1621 23.6201 0.7401 4.273 1.93e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.pCRTRG4/5 ctDNA (-) 1.906 0.52464 0.3819 9.513
ctDNA.pCRTRG4/5 ctDNA (+) 23.620 0.04234 5.5374 100.753
Concordance= 0.812 (se = 0.065 )
Likelihood ratio test= 18.73 on 2 df, p=9e-05
Wald test = 21.36 on 2 df, p=2e-05
Score (logrank) test = 38.75 on 2 df, p=4e-09#RFS by ctDNA at the MRD Window & ypTN Characteristics
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE,]
circ_data$DFS.months <- circ_data$DFS.months - 3
circ_data <- circ_data[circ_data$DFS.months >= 0,]
circ_data$ctDNA.pCR <- NA
# Define new categories
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage=="T1-T3" & ypNStatus == "N0" ~ "1",
ctDNA.MRD == "POSITIVE" | pT.Stage == "T4" | ypNStatus == "N1-N3" ~ "2"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data, conf.int = 0.95, conf.type = "log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE,
break.time.by = 12, palette = c("blue", "red"),
title = "RFS - ctDNA MRD/Clinical Characteristics",
ylab = "Recurrence-Free Survival", xlab = "Time from Landmark Time point (Months)",
legend.labs = c("MRD neg, T1-T3, N0", "MRD pos or T4 or N1-N3"),
legend.title = "")
summary(KM_curve, times = c(0, 24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2"), labels=c("MRD neg, T1-T3, N0","MRD pos or T4 or N1-N3"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)#OS by ctDNA at the MRD Window & ypTN Characteristics
# Clear workspace and set working directory
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE,]
circ_data$FU.months <- circ_data$FU.months - 3
circ_data <- circ_data[circ_data$FU.months >= 0,]
circ_data$ctDNA.pCR <- NA
# Define new categories
circ_data <- circ_data %>%
mutate(ctDNA.pCR = case_when(
ctDNA.MRD == "NEGATIVE" & pT.Stage=="T1-T3" & ypNStatus == "N0" ~ "1",
ctDNA.MRD == "POSITIVE" | pT.Stage == "T4" | ypNStatus == "N1-N3" ~ "2"
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data, conf.int = 0.95, conf.type = "log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE,
break.time.by = 12, palette = c("blue", "red"),
title = "OS - ctDNA MRD/Clinical Characteristics",
ylab = "Overall Survival", xlab = "Time from Landmark Time point (Months)",
legend.labs = c("MRD neg, T1-T3, N0", "MRD pos or T4 or N1-N3"),
legend.title = "")
summary(KM_curve, times = c(0, 24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2"), labels=c("MRD neg, T1-T3, N0","MRD pos or T4 or N1-N3"))
cox_fit <- coxphf(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)#Multivariate cox regression for RFS - ctDNA MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("Stomach","G/J"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + pT.Stage + ypNStatus + TRG, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for RFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#Multivariate cox regression for OS - ctDNA MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("Stomach","G/J"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + pT.Stage + ypNStatus + TRG, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#RFS by ctDNA at the Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.surveillance=NEGATIVE 33 10 NA 39.09 NA
ctDNA.surveillance=POSITIVE 4 4 6.27 3.21 NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA Surveillance window | All pts", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 33 0 1.000 0.0000 1.00 1.00
24 16 9 0.679 0.0896 0.47 0.82
ctDNA.surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 4 0 1 0 1 1 circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.surveillance, data = circ_data)
n= 37, number of events= 14
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.surveillancePOSITIVE 2.7504 15.6482 0.7292 3.772 0.000162 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.surveillancePOSITIVE 15.65 0.06391 3.748 65.34
Concordance= 0.657 (se = 0.06 )
Likelihood ratio test= 11.52 on 1 df, p=7e-04
Wald test = 14.23 on 1 df, p=2e-04
Score (logrank) test = 24.6 on 1 df, p=7e-07cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 15.65 (3.75-65.34); p = 0"#OS by ctDNA at the Surveillance Window - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.surveillance!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$FU.months, event = circ_data$OS.Event) ~
ctDNA.surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.surveillance=NEGATIVE 33 5 NA NA NA
ctDNA.surveillance=POSITIVE 5 3 8.66 5.54 NAsurv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 33 0 1.000 0.0000 1.000 1.000
24 20 3 0.886 0.0626 0.684 0.962
ctDNA.surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1 0 1 1 circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.surveillance, data = circ_data)
n= 38, number of events= 8
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.surveillancePOSITIVE 2.7378 15.4530 0.9239 2.963 0.00304 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.surveillancePOSITIVE 15.45 0.06471 2.527 94.5
Concordance= 0.727 (se = 0.088 )
Likelihood ratio test= 8.06 on 1 df, p=0.005
Wald test = 8.78 on 1 df, p=0.003
Score (logrank) test = 15.41 on 1 df, p=9e-05cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 15.45 (2.53-94.5); p = 0.003"---
title: "AGEO PLAGAST_Clinical Final analysis_revisions 05042025"
output: html_notebook
---
library(swimplot)
library(coxphf)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival) 
library(survminer) 
library(ggplot2)
library(scales)
library(coxphf)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(parameters)
library(car)
library(ComplexHeatmap)
library(tidyverse)
library(readxl)
library(survival)
library(janitor)
library(openxlsx)
library(writexl)
library(rms)
library(pROC)
library(DT)

#ctDNA Detection rate by Stage and Window
```{r}
#Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#C2D1
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.C2D1 %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.C2D1 == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.C2D1, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.C2D1 == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#post-NAC Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postNAC %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postNAC == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postNAC, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postNAC == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.MRD %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#On-treatment
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.onACT <- factor(circ_data$ctDNA.onACT, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.onACT %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.onACT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.onACT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.onACT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.surveillance %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Post-ACT Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postACT <- factor(circ_data$ctDNA.postACT, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postACT %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postACT == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postACT, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postACT == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Post-relapse Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$ctDNA.postRelapse <- factor(circ_data$ctDNA.postRelapse, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II","III"))
circ_data <- subset(circ_data, ctDNA.postRelapse %in% c("NEGATIVE", "POSITIVE"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.postRelapse == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.postRelapse, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.postRelapse == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```




#Neoadjuvant Treatment duration
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE, ]

circ_data$NAC.Start <- as.Date(circ_data$NAC.Start, format="%m/%d/%Y")
circ_data$NAC.End <- as.Date(circ_data$NAC.End, format="%m/%d/%Y")
circ_data$NAC.Duration <- as.numeric(difftime(circ_data$NAC.End, circ_data$NAC.Start, units="days"))
median_duration <- median(circ_data$NAC.Duration, na.rm=TRUE)
range_duration <- range(circ_data$NAC.Duration, na.rm=TRUE)
Q1 <- quantile(circ_data$NAC.Duration, 0.25, na.rm=TRUE)
Q3 <- quantile(circ_data$NAC.Duration, 0.75, na.rm=TRUE)
IQR_value <- IQR(circ_data$NAC.Duration, na.rm=TRUE)
cat("Median NAC Duration:", median_duration, "days\n")
cat("Range of NAC Duration:", range_duration[1], "-", range_duration[2], "days\n")
cat("Q1 (25th percentile):", Q1, "days\n")
cat("Q3 (75th percentile):", Q3, "days\n")
cat("Interquartile Range (IQR):", IQR_value, "days\n")
```

#Time of ctDNA draws from start and end of neodjuvant treatment
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE, ]

circ_data$NAC.Start <- as.Date(circ_data$NAC.Start, format="%m/%d/%Y")
circ_data$NAC.End <- as.Date(circ_data$NAC.End, format="%m/%d/%Y")
circ_data$ctDNA.C2D1.Date <- as.Date(circ_data$ctDNA.C2D1.Date, format="%m/%d/%Y")
circ_data$ctDNA.postNAC.Date <- as.Date(circ_data$ctDNA.postNAC.Date, format="%m/%d/%Y")

circ_data$Time_to_ctDNA_C2D1 <- as.numeric(difftime(circ_data$ctDNA.C2D1.Date, circ_data$NAC.Start, units="days"))
circ_data$Time_to_ctDNA_postNAC <- as.numeric(difftime(circ_data$ctDNA.postNAC.Date, circ_data$NAC.End, units="days"))

median_ctDNA_C2D1 <- median(circ_data$Time_to_ctDNA_C2D1, na.rm=TRUE)
range_ctDNA_C2D1 <- range(circ_data$Time_to_ctDNA_C2D1, na.rm=TRUE)
Q1_ctDNA_C2D1 <- quantile(circ_data$Time_to_ctDNA_C2D1, 0.25, na.rm=TRUE)
Q3_ctDNA_C2D1 <- quantile(circ_data$Time_to_ctDNA_C2D1, 0.75, na.rm=TRUE)
IQR_ctDNA_C2D1 <- IQR(circ_data$Time_to_ctDNA_C2D1, na.rm=TRUE)
median_ctDNA_postNAC <- median(circ_data$Time_to_ctDNA_postNAC, na.rm=TRUE)
range_ctDNA_postNAC <- range(circ_data$Time_to_ctDNA_postNAC, na.rm=TRUE)
Q1_ctDNA_postNAC <- quantile(circ_data$Time_to_ctDNA_postNAC, 0.25, na.rm=TRUE)
Q3_ctDNA_postNAC <- quantile(circ_data$Time_to_ctDNA_postNAC, 0.75, na.rm=TRUE)
IQR_ctDNA_postNAC <- IQR(circ_data$Time_to_ctDNA_postNAC, na.rm=TRUE)
cat("Median time from NAC start to ctDNA draw during NAC:", median_ctDNA_C2D1, "days\n")
cat("Range:", range_ctDNA_C2D1[1], "-", range_ctDNA_C2D1[2], "days\n")
cat("Q1 (25th percentile):", Q1_ctDNA_C2D1, "days\n")
cat("Q3 (75th percentile):", Q3_ctDNA_C2D1, "days\n")
cat("Interquartile Range (IQR):", IQR_ctDNA_C2D1, "days\n\n")

cat("Median time from NAC end to ctDNA draw post-NAC:", median_ctDNA_postNAC, "days\n")
cat("Range:", range_ctDNA_postNAC[1], "-", range_ctDNA_postNAC[2], "days\n")
cat("Q1 (25th percentile):", Q1_ctDNA_postNAC, "days\n")
cat("Q3 (75th percentile):", Q3_ctDNA_postNAC, "days\n")
cat("Interquartile Range (IQR):", IQR_ctDNA_postNAC, "days\n")
```

#Demographics Table
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    NAC,
    NAC.Regimen,
    TRG.Mandard,
    TNM,
    Stage,
    Grade,
    Lauren.Class,
    Margins,
    ACT,
    ACT.Regimen,
    DFS.Event,
    OS.Event,
    FU.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Stomach", "G/J", "Oesophagus")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Therapy", "Upfront Surgery")),
    NAC.Regimen = factor(NAC.Regimen),
    TRG.Mandard = factor(TRG.Mandard, levels = c("TRG1","TRG2", "TRG3", "TRG4", "TRG5")),
    TNM = factor(TNM, levels = c("T0-TisN0M0","T1-T2N0", "T2-T3N0-N1", "T2N1-N2", "T3N2-N3", "T4N0-N1", "T4N2-N3")),
    Stage = factor(Stage, levels = c("0","I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3")),
    Lauren.Class = factor(Lauren.Class),
    Margins = factor(Margins, levels = c("R0", "R1")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Treatment", "Observation")),
    ACT.Regimen = factor(ACT.Regimen),
    DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
    FU.months = as.numeric(FU.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE
)
fit1
save_as_docx(fit1, path= "~/Downloads/table1.docx")
```


#Demographics Table for those that included vs excluded from the study
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")

circ_data_subset1 <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    NAC,
    NAC.Regimen,
    TRG.Mandard,
    TNM,
    Stage,
    Grade,
    Lauren.Class,
    Margins,
    ACT,
    ACT.Regimen,
    DFS.Event,
    OS.Event,
    FU.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Stomach", "G/J", "Oesophagus")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Therapy", "Upfront Surgery")),
    NAC.Regimen = factor(NAC.Regimen),
    TRG.Mandard = factor(TRG.Mandard, levels = c("TRG1","TRG2", "TRG3", "TRG4", "TRG5")),
    TNM = factor(TNM, levels = c("T0-TisN0M0","T1-T2N0", "T2-T3N0-N1", "T2N1-N2", "T3N2-N3", "T4N0-N1", "T4N2-N3")),
    Stage = factor(Stage, levels = c("0","I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3")),
    Lauren.Class = factor(Lauren.Class),
    Margins = factor(Margins, levels = c("R0", "R1")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Treatment", "Observation")),
    ACT.Regimen = factor(ACT.Regimen),
    DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
    FU.months = as.numeric(FU.months))

circ_data1 <- read.csv("PLAGAST_Clinical Data.csv")

circ_data_subset2 <- circ_data %>%
  select(
    Age,
    Gender,
    PrimSite,
    NAC,
    NAC.Regimen,
    TRG.Mandard,
    TNM,
    Stage,
    Grade,
    Lauren.Class,
    Margins,
    ACT,
    ACT.Regimen,
    DFS.Event,
    OS.Event,
    FU.months,
    Included) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    PrimSite = factor(PrimSite, levels = c("Stomach", "G/J", "Oesophagus")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Therapy", "Upfront Surgery")),
    NAC.Regimen = factor(NAC.Regimen),
    TRG.Mandard = factor(TRG.Mandard, levels = c("TRG1","TRG2", "TRG3", "TRG4", "TRG5")),
    TNM = factor(TNM, levels = c("T0-TisN0M0","T1-T2N0", "T2-T3N0-N1", "T2N1-N2", "T3N2-N3", "T4N0-N1", "T4N2-N3")),
    Stage = factor(Stage, levels = c("0","I","II", "III")),
    Grade = factor(Grade, levels = c("G1", "G2", "G3")),
    Lauren.Class = factor(Lauren.Class),
    Margins = factor(Margins, levels = c("R0", "R1")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Treatment", "Observation")),
    ACT.Regimen = factor(ACT.Regimen),
    DFS.Event = factor(DFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
    FU.months = as.numeric(FU.months),
    Included = factor(Included, levels = c("TRUE", "FALSE"), labels = c("Included", "Excluded")))
Overall <- circ_data_subset1 %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
Overall

ByctDNA_MRD <- circ_data_subset2 %>%
  tbl_summary(
    by = Included, # Subgroup by Included
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  add_p(test = all_categorical() ~ "fisher.test", pvalue_fun = ~style_pvalue(p.adjust(.x, method = "bonferroni"))) %>%
  bold_labels()
ByctDNA_MRD

merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table

fit1 <- as_flex_table(
  merged_table,
  include = everything(),
  return_calls = FALSE)
fit1
save_as_docx(fit1, path= "~/Downloads/merged_table.docx")
```

#RFS by inclusion status
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~Included, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ Included, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - Inclusion status", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("Included", "Excluded"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$Included <- factor(circ_data$Included, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ Included, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by inclusion status
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~Included, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ Included, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - Inclusion status", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("Included", "Excluded"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$Included <- factor(circ_data$Included, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ Included, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#Heatmap with Clinical & Genomics Factors
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data %>% arrange(Stage)
circ_datadf <- as.data.frame(circ_data)

ha <- HeatmapAnnotation(
  Stage = circ_data$Stage,
  Gender = circ_data$Gender,
  PrimSite = circ_data$PrimSite,
  NAC = circ_data$NAC,
  ACT = circ_data$ACT,
  ctDNA.Base = circ_data$ctDNA.Base,
  ctDNA.C2D1 = circ_data$ctDNA.C2D1,
  ctDNA.postNAC = circ_data$ctDNA.postNAC,
  ctDNA.MRD = circ_data$ctDNA.MRD,
  ctDNA.surveillance = circ_data$ctDNA.surveillance,
  DFS.Event = circ_data$DFS.Event,
  OS.Event = circ_data$OS.Event,
  
  col = list(Stage = c("0" = "seagreen1", "I" = "seagreen1", "II" = "orange", "III" = "purple"),
    Gender = c("Female" = "goldenrod" , "Male" = "blue4"),
    PrimSite = c("Stomach" = "brown", "G/J" = "darkgreen", "Oesophagus" = "orange4"),
    NAC = c("FALSE" = "cornflowerblue", "TRUE" ="darkmagenta"),
    ACT = c("TRUE" = "brown4", "FALSE" ="khaki"),
    ctDNA.Base = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.C2D1 = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.postNAC = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.MRD = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    ctDNA.surveillance = c("POSITIVE" = "red3", "NEGATIVE" ="blue"),
    DFS.Event = c("TRUE" = "red3", "FALSE" ="blue"),
    OS.Event = c("TRUE" = "black", "FALSE" ="gray")
)
)
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$Stage)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()
```


#Overview Plot
```{r}
setwd("~/Downloads") 
clinstage<- read.csv("PLAGAST_OP.csv")
clinstage_df<- as.data.frame(clinstage)

#Display the swimmer plot with the label box
oplot<-swimmer_plot(df=clinstage_df,
                    id='PatientName',
                    end='fu.diff.months',
                    fill='gray',
                    width=.01,)
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(-12, 96, by = 6))
oplot <- oplot + labs(x ="Patients" , y="Months from Surgery")
oplot


##plot events
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
                                    id='PatientName',
                                    time='date.diff.months',
                                    name_shape ='Event_type',
                                    name_col = 'Event',
                                    size=3.5,fill='black',
                                    #col='darkgreen'
)
oplot_ev1

#Shape customization to Event_type

oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",values=c(1,16,6,18,4),breaks=c('ctDNA_neg','ctDNA_pos','Imaging','Surgery','Death'))

oplot_ev1.1

#plot treatment

oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
                                         id='PatientName',
                                         start='Tx_start.months',
                                         end='Tx_end.months',
                                         name_col='Tx_type',
                                         size=3.5,
                                         name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2  


#colour customization
oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "purple","black","black", "lightblue", "green", "red", "blue","orange"))
oplot_ev2.2
```


#RFS by ctDNA at Baseline - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Base, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA Baseline | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at Baseline - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Base, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Baseline | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#RFS by ctDNA levels at Baseline based on AUC optimal MTM/ml level - ctDNA Baseline positive
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)

#DFS.Event
circ_data <- circ_data[complete.cases(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM),]
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
ROC <- roc(DFS.Event ~  ctDNA.Base.MTM, data = circ_data, ci = TRUE)
p<-ggroc(ROC,
         aes = c("linetype"), color = "blue",  size = 1,
         legacy.axes = TRUE) +
  geom_abline(color = "dark grey", size = 0.5) +
  theme_classic()+
  ylab("Sensitivity") + theme(axis.title.x = element_text(color="black", size=14), axis.title.y = element_text(color="black", size=14),axis.text.x = element_text(colour = "black", size=14),axis.text.y = element_text(colour = "black",size=14),legend.title  = element_blank(),legend.text = element_text(size=14))
p

#AUC
AUC <- auc(ROC)
print(AUC)
AUC_conf <- ci.auc(ROC)
print(AUC_conf)
res.roc <- roc(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM)
plot.roc(res.roc, print.auc = TRUE, print.thres = "best")

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)

# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.6mMTM.Q = case_when(
    ctDNA.Base.MTM < 1.085 ~ 1,
    ctDNA.Base.MTM >= 1.085 ~ 2
  ))

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.6mMTM.Q, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA MTM/mL groups at Baseline", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<1.085", "MTM/mL≥1.085"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<1.085", "MTM/mL≥1.085"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#OS by ctDNA levels at Baseline based on AUC optimal MTM/mL level from RFS model - ctDNA Baseline positive
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)

# Create a new variable based on these quartiles

circ_data$ctDNA.6mMTM.Q <- NA
circ_data <- circ_data %>%
  mutate(ctDNA.6mMTM.Q = case_when(
    ctDNA.Base.MTM < 1.085 ~ 1,
    ctDNA.Base.MTM >= 1.085 ~ 2
  ))

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.6mMTM.Q, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA MTM/mL groups at Baseline", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<1.085", "MTM/mL≥1.085"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<1.085", "MTM/mL≥1.085"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#RFS by ctDNA at Baseline - excluding pts that did not receive NAC
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Base, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA Baseline | NAC treated only", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at Baseline - excluding pts that did not receive NAC
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Base, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Base, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Baseline | NAC treated only", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Base, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#RFS by ctDNA levels at Baseline based on AUC optimal MTM/ml level - ctDNA Baseline positive excluding pts that did not receive NAC
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)

#DFS.Event
circ_data <- circ_data[complete.cases(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM),]
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
ROC <- roc(DFS.Event ~  ctDNA.Base.MTM, data = circ_data, ci = TRUE)
p<-ggroc(ROC,
         aes = c("linetype"), color = "blue",  size = 1,
         legacy.axes = TRUE) +
  geom_abline(color = "dark grey", size = 0.5) +
  theme_classic()+
  ylab("Sensitivity") + theme(axis.title.x = element_text(color="black", size=14), axis.title.y = element_text(color="black", size=14),axis.text.x = element_text(colour = "black", size=14),axis.text.y = element_text(colour = "black",size=14),legend.title  = element_blank(),legend.text = element_text(size=14))
p

#AUC
AUC <- auc(ROC)
print(AUC)
AUC_conf <- ci.auc(ROC)
print(AUC_conf)
res.roc <- roc(circ_data$DFS.Event, circ_data$ctDNA.Base.MTM)
plot.roc(res.roc, print.auc = TRUE, print.thres = "best")

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- circ_data[circ_data$ctDNA.Base=="POSITIVE",]
circ_datadf <- as.data.frame(circ_data)

# Create a new variable based on these quartiles
circ_data$ctDNA.6mMTM.Q <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.6mMTM.Q = case_when(
    ctDNA.Base.MTM < 0.71 ~ 1,
    ctDNA.Base.MTM >= 0.71 ~ 2
  ))

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.6mMTM.Q, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6mMTM.Q, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA MTM/mL groups at Baseline", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("MTM/mL<0.71", "MTM/mL≥0.71"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.6mMTM.Q <- factor(circ_data$ctDNA.6mMTM.Q, levels=c("1","2"), labels = c("MTM/mL<0.71", "MTM/mL≥0.71"))
cox_fit <- coxph(surv_object ~ ctDNA.6mMTM.Q, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#RFS by ctDNA on-NAT - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.C2D1!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.C2D1, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA status on-NAT | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA on-NAT - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.C2D1!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.C2D1, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.C2D1, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA status on-NAT | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.C2D1 <- factor(circ_data$ctDNA.C2D1, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.C2D1, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#RFS by ctDNA post-NAT - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.postNAC, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postNAC, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA status post-NAT | All pts", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postNAC, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA post-NAT - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.postNAC, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.postNAC, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA status post-NAT | All pts", ylab= "Overall-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.postNAC, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#Multivariate cox regression for RFS - ctDNA post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("Stomach","G/J"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"), labels = c("Yes", "No"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.postNAC + PrimSite + Gender + Age.Group + ACT, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for RFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#Multivariate cox regression for OS - ctDNA post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.postNAC!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.postNAC <- factor(circ_data$ctDNA.postNAC, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("Stomach","G/J"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"), labels = c("Yes", "No"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.postNAC + PrimSite + Gender + Age.Group + ACT, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#Association of ctDNA Dynamics post-NAT and Response
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

#Vertical Fisher plot for ctDNA clearance post-NAT and Rec Status
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-", "ctDNA +/+/-", "ctDNA +/+/+"))
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$TRG)
fisher_exact_test <- fisher.test(contingency_table)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2

# Swapping x and y in ggplot function to make bar plot vertical
ggplot(table_df, aes(y = Var1, x = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(x = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA Dymamics post-NAT", y = "ctDNA", x = "Patients (%)", fill = "Path Response") +
  scale_x_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("TRG1/2/3" = "lightblue3", "TRG4/5" = "red")) + # define custom colors
  theme(axis.text.y = element_text(angle = 0, hjust = 1.5, size = 14), # increase y-axis text size
        axis.text.x = element_text(size = 14, color = "black"), # increase x-axis text size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculating p-value with Fisher exact test for the ctDNA +/+/+ vs ctDNA +/+/-
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

#Vertical Fisher plot for ctDNA clearance post-NAT and Rec Status
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="",]
circ_data <- circ_data[circ_data$ctDNA.Dynamics!="ctDNA +/-/-",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/+/-", "ctDNA +/+/+"))
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$TRG)
fisher_exact_test <- fisher.test(contingency_table)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
print(fisher_exact_test)
print(contingency_table)
```


#RFS by ctDNA Dynamics post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA Dynamics post-NAT", ylab= "Recurrence-Free Survival", xlab="Months from surgery", legend.labs=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
summary(cox_fit)
```


#OS by ctDNA Dynamics post-NAT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA Dynamics post-NAT", ylab= "Overall Survival", xlab="Months from surgery", legend.labs=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
cox_fit <- coxphf(surv_object ~ ctDNA.Dynamics, data=circ_data)
summary(cox_fit)
```


#Levels of Baseline MTM/mL in ctDNA Dynamics post-NAT categories
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "NEGATIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/-/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "NEGATIVE" ~ "ctDNA +/+/-",
    ctDNA.Base == "POSITIVE" & ctDNA.C2D1 == "POSITIVE" & ctDNA.postNAC == "POSITIVE" ~ "ctDNA +/+/+"
  ))

# Transform ctDNA.Base.MTM with log10
circ_data <- subset(circ_data, !is.na(ctDNA.Dynamics))
circ_data$ctDNA.Base.MTM <- as.numeric(as.character(circ_data$ctDNA.Base.MTM))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("ctDNA +/-/-","ctDNA +/+/-", "ctDNA +/+/+"))
median_Base_MTM <- aggregate(ctDNA.Base.MTM ~ ctDNA.Dynamics, data = circ_data, FUN = median)
print(median_Base_MTM)

# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=ctDNA.Dynamics, y=ctDNA.Base.MTM, fill=ctDNA.Dynamics)) +
  geom_violin(trim=FALSE) +
  scale_fill_manual(values=c("ctDNA +/-/-"="lightblue", "ctDNA +/+/-"="lightgreen", "ctDNA +/+/+"="salmon")) +
  geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
  scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
  labs(title="Baseline MTM/mL | Dynamics post-NAT", x="Dynamics post-NAT", y="Baseline MTM/mL") +
  theme_minimal() +
  theme(legend.position="none")
m3_1v2 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
                      data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/-/-", "ctDNA +/+/-"), ],
                      na.rm = TRUE)
print(m3_1v2)
m3_1v3 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
                      data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/-/-", "ctDNA +/+/+"), ],
                      na.rm = TRUE)
print(m3_1v3)
m3_2v3 <- wilcox.test(ctDNA.Base.MTM ~ ctDNA.Dynamics,
                      data = circ_data[circ_data$ctDNA.Dynamics %in% c("ctDNA +/+/-", "ctDNA +/+/+"), ],
                      na.rm = TRUE)
print(m3_2v3)
```

#Time of ctDNA draws at the MRD window from surgery
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE, ]

circ_data$ctDNA.MRD.Date <- as.Date(circ_data$ctDNA.MRD.Date, format="%m/%d/%Y")
circ_data$Surgery.Date <- as.Date(circ_data$Surgery.Date, format="%m/%d/%Y")
circ_data$Time_to_ctDNA_MRD <- as.numeric(difftime(circ_data$ctDNA.MRD.Date, circ_data$Surgery.Date, units="days"))
median_ctDNA_MRD <- median(circ_data$Time_to_ctDNA_MRD, na.rm=TRUE)
range_ctDNA_MRD <- range(circ_data$Time_to_ctDNA_MRD, na.rm=TRUE)
Q1_ctDNA_MRD <- quantile(circ_data$Time_to_ctDNA_MRD, 0.25, na.rm=TRUE)
Q3_ctDNA_MRD <- quantile(circ_data$Time_to_ctDNA_MRD, 0.75, na.rm=TRUE)
IQR_ctDNA_MRD <- IQR(circ_data$Time_to_ctDNA_MRD, na.rm=TRUE)
cat("Median time from surgery to ctDNA MRD time point:", median_ctDNA_MRD, "days\n")
cat("Range:", range_ctDNA_MRD[1], "-", range_ctDNA_MRD[2], "days\n")
cat("Q1 (25th percentile):", Q1_ctDNA_MRD, "days\n")
cat("Q3 (75th percentile):", Q3_ctDNA_MRD, "days\n")
cat("Interquartile Range (IQR):", IQR_ctDNA_MRD, "days\n")
```

#RFS by ctDNA at the MRD Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA MRD window | All pts", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at the MRD Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```





#RFS by ctDNA at the MRD Window - ACT pts only
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA MRD window | ACT treated", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at the MRD Window - ACT pts only
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | ACT treated", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```





#RFS by ctDNA at the MRD Window - NAC pts only
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="RFS - ctDNA MRD window | NAC treated", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at the MRD Window - NAC pts only
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$NAC==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | NAC treated", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```





#RFS by ctDNA at the MRD Window & ypT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="RFS - ctDNA MRD & ypTN", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)

#Repeat analysis to compare ctDNA MRD (-) vs (+) in T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="RFS - ctDNA MRD & ypTN", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#OS by ctDNA at the MRD Window & ypT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypTN", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)

#Repeat analysis to compare ctDNA post-NAT (-) vs (+) in T4
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T1-T3" ~ 1,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T1-T3" ~ 2,
    ctDNA.MRD == "NEGATIVE" & pT.Stage == "T4" ~ 3,
    ctDNA.MRD == "POSITIVE" & pT.Stage == "T4" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypTN", ylab= "Overall Survival Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & T1-T3", "ctDNA(+) & T1-T3", "ctDNA(-) & T4", "ctDNA(+) & T4"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("2","4","1","3"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#RFS by ctDNA at the MRD Window & ypN
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & ypNStatus == "N0" ~ 1,
    ctDNA.MRD == "POSITIVE" & ypNStatus == "N0" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
    ctDNA.MRD == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="RFS - ctDNA MRD & ypN", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#OS by ctDNA at the MRD Window & ypN
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Stage.II.Risk <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Stage.II.TNM = case_when(
    ctDNA.MRD == "NEGATIVE" & ypNStatus == "N0" ~ 1,
    ctDNA.MRD == "POSITIVE" & ypNStatus == "N0" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ypNStatus == "N1-N3" ~ 3,
    ctDNA.MRD == "POSITIVE" & ypNStatus == "N1-N3" ~ 4
  ))

circ_data <- circ_data[circ_data$ctDNA.Stage.II.TNM!="",]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.Stage.II.TNM, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Stage.II.TNM, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","purple", "red"), title="OS - ctDNA MRD & ypN", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.Stage.II.TNM <- factor(circ_data$ctDNA.Stage.II.TNM, levels=c("1","2","3","4"), labels = c("ctDNA(-) & ypN0", "ctDNA(+) & ypN0", "ctDNA(-) & ypN+", "ctDNA(+) & ypN+"))
cox_fit <- coxph(surv_object ~ ctDNA.Stage.II.TNM, data=circ_data) 
summary(cox_fit)
```

#RFS by ctDNA at the MRD Window & TRG combination (TRG1/2/3 vs TRG3-5)
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))

circ_data$ctDNA.pCR <- NA
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.MRD == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
    ctDNA.MRD == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
    ctDNA.MRD == "POSITIVE" & TRG == "TRG4/5" ~ "3"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="RFS - ctDNA MRD/TRG", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
```


#OS by ctDNA at the MRD Window & TRG combination (TRG1/2/3 vs TRG3-5)
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_data$TRG <- factor(circ_data$TRG, levels = c("TRG1/2/3", "TRG4/5"))

circ_data$ctDNA.pCR <- NA
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.MRD == "NEGATIVE" & TRG == "TRG1/2/3" ~ "1",
    ctDNA.MRD == "NEGATIVE" & TRG == "TRG4/5" ~ "2",
    ctDNA.MRD == "POSITIVE" & TRG == "TRG4/5" ~ "3"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","green","red"), title="OS - ctDNA MRD/TRG", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"), legend.title="")
summary(KM_curve, times= c(0,24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2","3"), labels=c("TRG1/2/3 ctDNA (-)","TRG4/5 ctDNA (-)", "TRG4/5 ctDNA (+)"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
```


#RFS by ctDNA at the MRD Window & ypTN Characteristics
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE,]
circ_data$DFS.months <- circ_data$DFS.months - 3
circ_data <- circ_data[circ_data$DFS.months >= 0,]
circ_data$ctDNA.pCR <- NA

# Define new categories
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage=="T1-T3" & ypNStatus == "N0" ~ "1",
    ctDNA.MRD == "POSITIVE" | pT.Stage == "T4" | ypNStatus == "N1-N3" ~ "2"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.pCR, data = circ_data)
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data, conf.int = 0.95, conf.type = "log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, 
           break.time.by = 12, palette = c("blue", "red"), 
           title = "RFS - ctDNA MRD/Clinical Characteristics", 
           ylab = "Recurrence-Free Survival", xlab = "Time from Landmark Time point (Months)", 
           legend.labs = c("MRD neg, T1-T3, N0", "MRD pos or T4 or N1-N3"), 
           legend.title = "")
summary(KM_curve, times = c(0, 24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2"), labels=c("MRD neg, T1-T3, N0","MRD pos or T4 or N1-N3"))
cox_fit <- coxph(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#OS by ctDNA at the MRD Window & ypTN Characteristics
```{r}
# Clear workspace and set working directory
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included == TRUE,]
circ_data$FU.months <- circ_data$FU.months - 3
circ_data <- circ_data[circ_data$FU.months >= 0,]
circ_data$ctDNA.pCR <- NA

# Define new categories
circ_data <- circ_data %>%
  mutate(ctDNA.pCR = case_when(
    ctDNA.MRD == "NEGATIVE" & pT.Stage=="T1-T3" & ypNStatus == "N0" ~ "1",
    ctDNA.MRD == "POSITIVE" | pT.Stage == "T4" | ypNStatus == "N1-N3" ~ "2"
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.pCR),]
survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.pCR, data = circ_data)
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.pCR, data = circ_data, conf.int = 0.95, conf.type = "log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, 
           break.time.by = 12, palette = c("blue", "red"), 
           title = "OS - ctDNA MRD/Clinical Characteristics", 
           ylab = "Overall Survival", xlab = "Time from Landmark Time point (Months)", 
           legend.labs = c("MRD neg, T1-T3, N0", "MRD pos or T4 or N1-N3"), 
           legend.title = "")
summary(KM_curve, times = c(0, 24))
circ_data$ctDNA.pCR <- factor(circ_data$ctDNA.pCR, levels=c("1","2"), labels=c("MRD neg, T1-T3, N0","MRD pos or T4 or N1-N3"))
cox_fit <- coxphf(surv_object ~ ctDNA.pCR, data=circ_data)
summary(cox_fit)
```

#Multivariate cox regression for RFS - ctDNA MRD Window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("Stomach","G/J"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + pT.Stage + ypNStatus + TRG, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for RFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#Multivariate cox regression for OS - ctDNA MRD Window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels=c("Stomach","G/J"))
circ_data$Gender <- factor(circ_data$Gender, levels=c("Male","Female"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels=c("2","1"), labels = c(">70", "≤70"))
circ_data$pT.Stage <- factor(circ_data$pT.Stage, levels=c("T1-T3","T4"))
circ_data$ypNStatus <- factor(circ_data$ypNStatus, levels=c("N0","N1-N3"))
circ_data$TRG <- factor(circ_data$TRG, levels=c("TRG1/2/3","TRG4/5"))
surv_object <- Surv(time = circ_data$FU.months, event = circ_data$OS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + PrimSite + pT.Stage + ypNStatus + TRG, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#RFS by ctDNA at the Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-3
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.surveillance, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="RFS - ctDNA Surveillance window | All pts", ylab= "Recurrence-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




#OS by ctDNA at the Surveillance Window - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("PLAGAST_Clinical Data.csv")
circ_data <- circ_data[circ_data$Included==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.surveillance!="",]
circ_data$FU.months=circ_data$FU.months-3
circ_data <- circ_data[circ_data$FU.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$FU.months, event = circ_data$OS.Event)~ctDNA.surveillance, data = circ_data)
surv_object <-Surv(time = circ_data$FU.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=12, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All pts", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.surveillance <- factor(circ_data$ctDNA.surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```




